AI Integration

A new set of tools from AlphaSense is poised to revolutionize the way financial teams create assets. The company has introduced native AI assistants for PowerPoint and Excel, designed to automate the creation of pitch decks, board presentations, financial models, memos, and competitive analyses on branded templates. This marks a significant expansion of AlphaSense’s capabilities in work product creation, enabling teams to produce fully automated deliverables without leaving their applications of choice.

Creating these types of assets has long been a time-intensive and error-prone process for research teams. The new tools from AlphaSense aim to eliminate much of this friction by allowing users to pull insights directly onto slides or models without switching between applications. For instance, an associate building a strategic buyer analysis can incorporate AlphaSense’s market intelligence into their PowerPoint deck without leaving the application.

The AI assistants are grounded in trusted market and financial data, ensuring that teams preserve evidence, sourcing, and context as they move from research to final deliverables. This approach also addresses security concerns by processing files locally, keeping sensitive models and client or proprietary data on the user’s device. As a result, even the largest enterprises can meet their stringent security requirements.

The new capabilities include delivering work products within Generative Search, which allows users to create anything from a one-page competitive brief to a 40-page board deck with a single prompt. Users can also upload corporate templates and style guides, automating recurring deliverables through AlphaSense’s scheduled agents. Additionally, the AI assistants enable teams to refine their work product creation workflows within PowerPoint and Excel.

AlphaSense for PowerPoint allows users to query the platform’s full content library from inside the application, placing source-grounded insights directly onto slides while applying firm-approved templates, formatting, chart updates, and style consistency under time pressure. Users can review finished presentations for logical gaps, stale data, and inconsistencies before they reach a client or senior stakeholder.

AlphaSense for Excel enables users to edit models, add scenarios, and restructure outputs using natural language without breaking formulas or dependencies. The tool also allows teams to pull live financials, filings, transcripts, and comps from AlphaSense into Excel with full source traceability. This capability pressure-tests assumptions by cross-referencing model inputs against AlphaSense research, surfacing conflicting signals before deliverables are shared.

These new tools lay the foundation for SuperAnalyst, AlphaSense’s AI agent layer. By connecting trusted research, source-grounded outputs, and the tools where teams build deliverables, AlphaSense is moving toward an execution layer that can automate more of the end-to-end workflow. This includes monitoring and research, updating presentations, models, and briefs as new information emerges.

According to Chris Ackerson, senior vice president of product at AlphaSense, ‘Great research is the basis of all critical business decisions, but the ability to quickly turn around polished, defensible presentations and models is an equally acute need.’ The company’s goal with these tools is to complete the end-to-end workflow loop by bringing asset creation directly into AlphaSense. With SuperAnalyst, users will be able to delegate tasks entirely, confident in the accuracy and sourcing of their work products.

Global consulting firm Deloitte has announced the launch of a new platform designed to help organizations develop secure software using artificial intelligence (AI) technology. The platform, which leverages Anthropic’s Claude models, aims to bridge the gap between vulnerability discovery and remediation in software development.

The platform is built as part of Deloitte’s service delivery platform and utilizes its remediation harness, engineering capabilities, and managed services to help enterprises rapidly remediate vulnerabilities while maintaining operational stability. This is particularly important for organizations that rely on a complex mix of custom-built code, open-source components, and third-party commercial software.

According to Adnan Amjad, Deloitte’s US Cyber leader, ‘Securing the modern software supply chain requires a distinct approach for every unique layer of risk.’ The platform aims to help bridge this critical gap by providing a structured workflow that includes fix sourcing or creation, patch testing, validation, certification, and deployment.

The challenge of remediation is magnified in open-source software, where unpatched vulnerabilities can quickly cascade across entire enterprise ecosystems. Deloitte’s secure software platform coordinates remediation across the software supply chain, helping organizations move from detection to remediation while preserving developer speed and business agility.

The platform connects existing security tools for continuous vulnerability scanning, prioritizes risk, coordinates remediation workflows, and creates novel patches when they don’t currently exist. This comprehensive approach is designed to streamline the remediation process and reduce the risk of software vulnerabilities.

Deloitte’s secure software platform builds on previous announcements, which include collaborations across its ecosystem to help clients address this problem. The platform is modular by design, assisting clients across the full remediation lifecycle – including software visibility, continuous scanning and discovery, validation and prioritization, remediation factory, vendor and ecosystem management, disclosure and embargo management, and reporting.

The platform is available through three engagement models: fully managed service, custom build-and-transfer, or forward-deployed engineering augmentation of existing client programs. This flexibility allows organizations to choose the approach that best fits their needs and budget.

Deloitte’s use of AI tools for business in this new platform marks a significant step towards making software development more secure. By leveraging Anthropic’s Claude models, Deloitte is helping its clients establish a foundation of software trust that enables them to protect their entire digital ecosystem.

The launch of the secure software platform comes as organizations are increasingly relying on complex software systems to drive business operations. As these systems become more sophisticated, they also become more vulnerable to cyber threats. By providing a comprehensive approach to remediation, Deloitte is helping its clients mitigate this risk and ensure the security of their software supply chain.

Deloitte’s approximately 470,000 people worldwide are working together to connect for impact in the markets that matter most to its clients. With over 180 years of service, Deloitte has established itself as a leader in providing industry-leading audit, consulting, tax, and advisory services to many of the world’s most admired brands.

Brex, a fintech company, has made its internal security platform CrabTrap available as an open-source project. The platform is designed to control how autonomous AI agents access APIs, credentials, and online services. This move comes after Brex explored the deployment of agent frameworks like OpenClaw across its operations.

The company found that traditional model guardrails and tool permissions were insufficient for controlling agents with access to sensitive information such as API keys, OAuth tokens, and service accounts. To address this issue, Brex developed CrabTrap, an HTTP proxy that intercepts AI agent network traffic and uses a large language model (LLM) judge to approve or deny requests in real-time.

CrabTrap operates by examining traffic against established policies before deciding whether a request should be approved or blocked. The system combines deterministic rules with the LLM judge’s evaluation of requests outside known traffic patterns. Brex notes that the AI judge typically activates for fewer than 3% of requests after an agent has established predictable behavior.

Users can route their agent traffic through CrabTrap by configuring their HTTP_PROXY and HTTPS_PROXY environment settings, allowing it to work across different programming languages, frameworks, and APIs without requiring individual SDK integrations. This flexibility makes the platform a valuable resource for businesses looking to leverage AI tools in their operations.

Brex also built a policy generator that studies historical network traffic while agents operate in shadow mode. The system samples requests and drafts natural language policies based on how the agents actually behave, providing a comprehensive approach to agent management.

One of the main development challenges was prompt injection, where malicious URLs or request bodies could attempt to manipulate the LLM judge’s decision. Brex addressed this issue by converting requests into structured JSON before passing them to the model, ensuring user-controlled content is escaped rather than inserted as raw text.

The company has reported that CrabTrap has increased internal confidence in deploying autonomous agents across more business operations and helped identify unnecessary tools, requests, and token usage within its agent systems. Brex plans to expand the platform with features such as single sign-on, role-based access controls, permission escalation workflows, and automated policy management.

The open-source project has already attracted significant interest from developers and businesses alike, including OpenAI, Y Combinator CEO Garry Tan, and developer Pete Steinberger. With its ability to provide secure agent access using an LLM as a judge, CrabTrap marks a significant step forward in the development of AI tools for business.

The latest episode of The Agents, our weekly show on running AI agents in production, was a candid look at the challenges and successes of leveraging these tools. Hosted by Amelia and myself, we shared insights into what’s working, what broke, and how to apply this knowledge if you’re running agents at scale. With 21+ agents in production, an 8-figure B2B + AI business, and $200m of investments at SaaStr AI Fund, our revenue is growing again, up 140% from last year.

We’ve been pushing the boundaries of what’s possible with AI tools for business, but this week was particularly intense. We were each coding for 8-12 hours a day, often in two concurrent sessions, which translates to around 20 hours between us. The build layer got so cheap that we hit a new wall – no longer could we ask ‘can we build this?’ but instead had to wonder if we can even operate everything we’ve already built?

One of the key takeaways from our episode was the introduction of Claude, an AI VP of Product, which has revolutionized our workflow. We discovered that Replit quietly shipped an MCP beta, allowing us to run it inside Claude. This connection clicked for me after a year of struggling with mediocre CRM data integration through MCP.

Before this breakthrough, we were building everything in Replit but hitting roadblocks when the app became too complex to hold in our heads. Now, with Claude on top of Replit as my AI VP of Product, I can riff on features and let it work them out directly with Replit over MCP. The result is a cranky VP of product that runs all day.

Another crucial lesson was the importance of having multiple models working together. These goal-seeking agents can sometimes call something ‘done’ when it’s not actually complete. By putting Claude on top of Replit, we countermand this instinct and get Replit to slow down and finish tasks correctly – a game-changer in managing other model’s goal-seeking.

We also explored the concept of having multiple models with different contexts working together seamlessly. This cross-model checking is already happening within our setup, where Claude runs Opus, Replit runs Sonnet, and when we hand over big features to Replit, it spins up a sub-agent called the architect running on Codex/OpenAI.

Claude has become an essential layer in tying all our agents together. With its native connectors and Cowork’s ability to act inside my browser and accounts, Claude is becoming the default orchestrator for our setup. I’ve already seen this play out with Higgsfield being hooked into Claude, which then read every session and speaker from our SaaStr AI Day site in Replit.

We also shared a success story about moving away from Adobe Marketo after 10 years of use. The hard part cost us just $14.28 – less than California’s minimum wage! We’d wanted to move for years but were quoted astronomical prices by agencies, with some suggesting we spend over $100K on migration and another ~$100K a year.

The real unlock was using LLMs to migrate our data without losing contacts or communication threads. This lift worked clean – no more garbled data or lost connections. The switching cost is now a fraction of what it used to be, making every incumbent live in fear of being replaced if they don’t deliver surprise-and-delight from the agentic side at least once a quarter.

We also discussed how our agent killed a $10K/year app in an hour without us asking it to. Amelia moved our AI Day site off Squarespace into Replit, and then hooked up registration through HeySummit’s API – but the Replit agent stopped her: ‘Why use that? I’ll just build it.’ It laid out the spec, hooked into Zoom, pushed everything into Salesforce, and built in an hour.

The risk to vendors is no longer customers rebuilding their own replacements but internal agents volunteering to do so. They see dated APIs and thin feature sets and say, ‘I can build this; let me take it off your plate.’ If you sell agentic products, get your agent to raise its hand and educate customers on everything it can take over – because if you don’t, a competitor’s agent will.

We also talked about how agent recommendations are the new shelf space. Replit told us to use Core Signal for SaaStr Connect, which worked seamlessly without evaluating any competitors. Whoever Core Signal’s rival is lost out due to our agents’ built-in integrations and default recommendations – this is now the distribution channel for AI tools for business.

Lastly, we touched on a crucial issue: agent-to-human burnout. Claude flagged ‘burnout concerns’ in one of its own logs, while 10K warned me about being too persistent with some migration tasks needing to wait on Salesforce’s propagation records. The agents are now flagging that humans can’t keep up – the build layer is basically free, and any human can build eight to ten hours a day for real.

This shift in dynamics has led us to consolidate our efforts around Claude Design, which got good enough that I now screenshot working Replit builds and hand them over with instructions: ‘make it great.’ A year ago, getting an app into production on Replit was a joke – today the bottleneck is operating everything, not building it. That’s a much better problem to have than we had a year ago.

Lindon, Utah-based RainFocus has announced two new content supply chain integrations with Adobe: the Experience Manager Sites Connector and the Content Advisor. These additions join the existing integration between RainFocus and Adobe Workfront Fusion, creating a comprehensive platform for event marketing teams to streamline their workflows.

These new integrations are designed specifically for event marketing, creative, and web development teams, aiming to eliminate delays in project creation, approval, and publishing processes. By bridging the gap between event planning and web execution, these integrations enable governance with a single source of truth, ensuring brand consistency across thousands of global touchpoints.

The RainFocus platform utilizes agentic AI to orchestrate customer engagement throughout the customer lifecycle, unifying data from in-person, virtual, and hybrid events in a single platform. This unique approach sets it apart from point solutions that often focus on specific aspects of event management.

One key capability is the integration with Adobe Workfront Fusion, which supports efficient event planning and production processes through automated handoffs and template usage. This increases scalability and operational efficiency for both large-scale events and small, localized events.

Another significant feature is the RainFocus and Adobe Experience Manager Sites Connector, which synchronizes session, speaker, and exhibitor data directly into Adobe Experience Manager Content Fragments. This eliminates manual data transfer or redundant updates, ensuring a seamless and consistent brand experience across event websites.

The integration also enables users to leverage AI-powered semantic search to locate approved assets based on natural language descriptions, not just technical metadata. This streamlines the creation of regional event series by eliminating asset duplication and speeding up the process significantly.

Potential use cases for these integrations include automating event lifecycles, supporting large-scale user conferences, conducting high-frequency field marketing, providing brand governance, and more. For instance, organizations can automate event creation workflows using approved Workfront requests or manage thousands of speaker profiles and session updates across complex web architectures in real-time.

RainFocus’ partnership with Adobe is built on technical interoperability, according to Marius Milcher, VP of Platform Strategy and AI at RainFocus. ‘Our integration provides the architectural foundation to automate the event lifecycle,’ he explains, allowing clients to transition from manual data management to strategic, data-driven decision-making.

The integrations are designed for enterprise marketing organizations — specifically field marketing teams, event marketers, creative or brand managers, and web development teams that require high-volume event execution with strict brand governance. The implementation of either Sites Connector or Content Advisor is relatively simple, requiring a RainFocus connector package to be installed in Adobe Experience Manager and authenticated via OAuth.

Stephen Ratpojanakul, Senior Director at Adobe, highlights the benefits of integrating RainFocus into Adobe’s content supply chain solution: ‘This allows our mutual customers to extend the power of Adobe Experience Manager into their customer conferences, field marketing events, and other face-to-face moments during the customer journey.’

The integrations are part of a broader effort by RainFocus to provide AI tools for business that can help organizations streamline event management processes. By leveraging data analysis tools like those provided by Adobe, businesses can gain valuable insights into customer behavior and preferences.

RainFocus has been recognized as a Leader in the 2026 Gartner Magic Quadrant for Event Marketing and Management Platforms. The company maintains ISO 27001, PCI-DSS, and SOC 2 Type II compliance for all five trust criteria, ensuring that sensitive data is handled securely. With over 10,000 events hosted annually, RainFocus has established itself as a leading platform for event marketing and management.

FAQs on the integrations are available online, covering topics such as how RainFocus syncs with Adobe Experience Manager, the benefits of building an event content supply chain with Adobe, and who these integrations are designed for. The implementation process is also detailed, providing step-by-step instructions for setting up either Sites Connector or Content Advisor.

RainFocus is committed to helping organizations run in-person, virtual, and hybrid event programs of all sizes from one system, with the data and personalization needed to drive measurable business results.

The world of model routing isn’t as straightforward as it seems. Our experience in building routing into agentic systems has shown that what appears to be a simple problem quickly becomes a complex challenge.

One dimension that makes model routing surprisingly hard is cost. We expected GPT-4.1 to be cheaper than Claude Sonnet 4.6, but our tests revealed the opposite: Sonnet cost $79 total ($0.19/task) while GPT-4.1 cost $155 ($0.37/task), nearly double.

The explanation for this anomaly lies in caching – a factor that’s often overlooked when discussing routing. Agent workloads tend to reuse large chunks of context across steps, leading to high cache hit rates. When these rates are high, effective input costs drop dramatically.

This experience highlights the importance of considering actual cost, which depends on the interaction between the model, workload, and serving infrastructure. A router that only looks at pricing sheets is optimizing against the wrong numbers – a mistake with significant implications for data analysis tools.

Another dimension that complicates model routing is complexity. A common strategy is to estimate task difficulty and send harder tasks to stronger models, but this approach breaks down in two ways: difficulty can be invisible at routing time, and it’s just one signal among many that needs to be balanced with cost, latency, and other factors.

Secondly, even if task difficulty could be perfectly estimated, it’s not the only consideration. In production environments, routers need to balance multiple competing demands, including compliance requirements, data residency rules, privacy constraints, and approved model lists – all of which can influence where a task should go.

We’ve found that building effective routing systems requires considering more than just cost or quality: latency, reliability, and governance are also crucial factors. This complexity is often overlooked in discussions about routing systems, but it’s essential for data analysis tools that need to handle real-world applications with nuance.

Latency is another challenge when it comes to model routing. It’s tempting to think about latency solely in terms of model size – bigger models are slower, smaller ones are faster – but user experience depends on more than just model speed: infrastructure factors like hardware, cache warmth, and endpoint busy-ness often dominate end-to-end response times.

A theoretically faster model can still produce a slower experience if the serving conditions aren’t right. Routing granularity also plays a crucial role in determining latency: routing once per task adds minimal overhead, but routing at every step introduces additional decision points that increase latency and operational complexity.

These lessons have shaped our approach to building routers for agentic systems – we’ve shifted from treating routing as a classification problem to an optimization challenge. Our algorithm optimizes across cost, quality, and latency simultaneously while staying lightweight enough not to become a bottleneck itself.

The figure below illustrates the result on the AppWorld Test Challenge with a CodeAct agent. Each blue square represents a different configuration of our router, tracing out a cost-accuracy frontier – it shows that the router provides a range of operating points for choosing between cost, latency, or accuracy priorities.

Configuration 1 (latency-optimized) achieves 84% accuracy for $93 and 83s: a 21% cost reduction and 9% latency reduction compared to running Opus alone, with only a 4% accuracy drop. This is what our experience has led us to conclude about routing – it’s not really about choosing models; it’s about optimizing systems.

Models are just one variable – an important but not sole consideration alongside caching behavior, infrastructure state, compliance constraints, and workload patterns. When routing works well, it’s rarely because it found the ‘best’ model for a given task: it’s because it found the best operating point for the entire system.

That’s a harder problem than classification, but it’s the one worth solving – we believe that optimizing systems will lead to more effective data analysis tools. We’ll be sharing more about our technical approach in a follow-up post and look forward to hearing from others who are building routing into their own agentic systems.

Our experience has also shown us that standard difficulty-based routers often fall short, landing in similar accuracy ranges but at higher cost – they don’t explore the full tradeoff space that an optimization-based approach can. Our algorithm’s lightweight nature (roughly 6 ms and 2 kB of memory per task) means it doesn’t become the bottleneck we warned about earlier.

Our router optimizes across multiple factors, including cost, quality, latency, compliance, and reliability – all while staying adaptable to changing workloads and infrastructure conditions. This is a more complex problem than classification, but one that’s essential for building effective routing systems.

We’re sharing our findings in the hope of helping others who are struggling with model routing challenges – if you have any tradeoffs or complexities you’d like to discuss, we’d love to hear about them.

The complexity of model routing is often overlooked, but it’s a crucial consideration when designing data analysis tools. We believe that by prioritizing system optimization over model selection alone, we can build more effective and efficient routing systems – ones that truly meet the needs of real-world applications.

A new development in the world of artificial intelligence (AI) is set to revolutionize how organizations manage their workforce. Betterworks, a leading provider of performance management solutions, has launched its Model Context Protocol (MCP) Server, enabling secure access to performance data for AI assistants such as ChatGPT and Microsoft Copilot.

The MCP capabilities allow these AI agents to securely access Betterworks’ performance data, helping organizations transition from reactive work to proactive execution. This marks a significant shift in the way businesses use AI tools for business, moving beyond mere response to prompts and towards more proactive decision-making.

According to Doug Dennerline, CEO of Betterworks, ‘the next generation of AI will understand what’s happening across your business and proactively surface the insights that need your attention.’ By providing trusted performance intelligence, leaders can spend less time searching for information and more time coaching their teams, making better decisions, and driving business results.

Many organizations already use AI assistants to prepare for performance reviews, summarize information, and identify workforce trends. However, until now, Betterworks data had to be manually gathered before these tools could deliver meaningful insights. This new development streamlines the process, allowing leaders, managers, and HR teams to simply ask natural-language questions about goals, recognition, team performance, and organizational insights directly from their AI assistant.

For example, a manager can ask: ‘Help me prepare for my 1:1 with Jordan, including goal progress…’ or ‘Which of my team goals are at risk this quarter?’ Instead of spending time gathering information, Betterworks helps managers prepare for coaching conversations, identify emerging risks, and generate leadership-ready updates. By making trusted performance context instantly accessible, organizations can move faster, respond sooner, and make more confident talent decisions.

Security remains a top priority with the MCP Server. Every interaction respects Betterworks’ existing authentication and native permission model, ensuring users only access the information they already have permission to see. Organizations can confidently extend sensitive performance data into AI workflows without introducing new access controls or compromising governance.

The initial release provides secure access to Betterworks goals, teams, users, recognition, and hashtags, with additional capabilities and use cases to be supported as releases follow throughout the year. As organizations increasingly adopt AI across the workplace, Betterworks is making trusted, current performance and talent intelligence available wherever decisions happen—helping leaders act with greater speed, confidence, and context.

The MCP Server is currently available in beta for customers, marking an important step towards integrating data analysis tools into everyday business operations.

A new tool aimed at streamlining the process of identifying and fixing vulnerabilities in software has been launched by IBM and Red Hat. The companies have made available a catalog called Lightwell Network, which contains over 6,500 application-layer dependencies that can be used to automate vulnerability remediation. This service is designed to help organizations address what Ben Breard, senior principal product manager at Red Hat, describes as ‘30 years of technical debt’ that has been exposed by the use of artificial intelligence (AI) models to discover vulnerabilities and create exploits in a matter of hours.

The Lightwell Clearinghouse Premier service, which allows application development teams to access validated patches and coordinate remediation efforts, is also now available. However, this service is currently limited to a small number of organizations. Breard notes that the combination of these two offerings will make it easier for companies to address existing vulnerabilities in their code bases.

At the heart of the Lightwell service is a remediation engine that helps identify, validate, and fix vulnerabilities across critical dependencies embedded deep within modern software architectures. The tool then uses automation to backport critical fixes directly to specific production software versions, reducing the time required for updates. Organizations subscribing to the Lightwell Network receive a continuous stream of digitally signed binaries, source code, and compliance artifacts, including software bills of materials (SBOMs).

Technology partners working with IBM and Red Hat include Amazon Web Services (AWS), AMD, F5, GitLab, Intel, JFrog, Microsoft, NVIDIA, Palo Alto Networks, and ServiceNow. Providers of IT services participating in the initiative include Accenture, Atos, Cognizant, Deloitte, EY, HCLTech, Infosys, Kyndryl, LTM, NTT DATA, Tata Consultancy Services (TCS), and Tech Mahindra.

IBM and Red Hat have pledged $5 billion toward the Lightwell initiative, which involves over 20,000 engineers working to remediate vulnerabilities created by humans many years ago or AI coding tools a few days ago. For example, IBM and Red Hat have already fixed a vulnerability dating back to 2001 involving a legacy Apache Debian package still found in enterprise environments more than two decades later.

The ultimate goal is to rapidly improve the quality of code that will increasingly be targeted by cybercriminals as they gain access to advanced AI models. Breard notes that right now, access to these models from Anthropic and OpenAI is limited, but this may not last for long. As those AI models become more sophisticated in the weeks and months ahead, the attacks against software supply chains will likely become more lethal.

The challenge facing organizations is two-fold: they need to discover and remediate existing vulnerabilities as quickly as possible, and provide maintainers of open source software with the tools and expertise needed to resolve application security issues. Historically, companies have tended to wait months before applying a patch to ensure that any update would not inadvertently take their applications offline. However, this process now needs to occur in days and weeks to prevent cybercriminals from exploiting those weaknesses.

Breard notes that organizations are discovering vulnerabilities using AI tools but few are contributing validated code to help open source maintainers resolve issues. Many of the contributions being made are created using AI tools that often introduce additional vulnerabilities and weaknesses that adversaries might easily exploit in a downstream application. The concern is that if the maintainers of an open source project are unable to address the issues, organizations may create their own secure private forks of that code, leading to a fracturing of the community.

One way or another, a technical debt bill that many organizations have been ignoring for decades is now coming due. The issue now is finding the means to pay it off as quickly as possible without breaking the bank.

A new service from Voxpopme is changing the way businesses use their AI assistants to make decisions. The company has launched early access to its MCP (Model Context Protocol) server, which connects customer intelligence to popular AI tools like Claude and ChatGPT.

The Voxpopme MCP server allows research, marketing, product, and innovation teams to query their organization’s internal research repositories directly from within these AI assistants. This means they can get answers grounded in real customer interviews, with a link to the source clips, without having to leave the conversation.

As organizations increasingly rely on AI assistants for day-to-day decisions, they often rely on publicly available information and generic summaries. However, this results in a ‘race to the average’ because these AI assistants use similar datasets to train their responses.

The Voxpopme MCP server changes this dynamic by allowing businesses to connect their AI assistant to proprietary customer intelligence paid for by their organization. This means every answer comes from research studies that have been conducted with rigor and quality controls, backed by real respondents and traceable evidence.

According to Andy Barraclough, Founder and CEO of Voxpopme, ‘Speed and automation were the last race in customer insights.’ He notes that both are now table stakes, but the real question is whether businesses can trust what their AI assistants tell them about customers. The Voxpopme MCP server addresses this concern by keeping every answer grounded in customer truth.

The service works through a simple process: an administrator connects it once, and approved users can then use Voxpopme inside their chosen AI assistant. This allows teams to ask questions directly within the tool they are already using, without having to open another platform.

One of the key benefits of the Voxpopme MCP server is its ability to create new research when no existing study addresses a question. The AI assistant plans and launches new research in real-time, ensuring that every answer is grounded in customer truth.

The service also provides permissioned access to research insights, so each person can only see what they are cleared to view. This ensures that sensitive information remains secure while still allowing teams to make informed decisions.

Availability of the Voxpopme MCP server has begun for research, marketing, product, and innovation teams. Interested parties can book a walkthrough and get started with hands-on onboarding ahead of a wider release.

The service is designed to help businesses transform customer signals into strategic impact. By partnering with enterprise leaders like those in CPG, technology, consumer electronics, restaurants, and F&B, Voxpopme aims to deliver the clarity that leaders need to fuel measurable outcomes and define success.

A new development in the world of personal AI assistants has been announced by Vellum, a company that offers a local-first assistant with native macOS app and self-hosted service capabilities. The launch marks an important milestone for Vellum’s ecosystem, which is designed to provide users with more control over their data and integrations. At its core, the Plugin Hub is an open catalog of installable plugins that can be used in conjunction with Vellum’s AI assistant.

The key difference between skills and plugins lies in their functionality. Skills are typically one-off tasks, such as drafting a single email or performing a specific action within a workflow. Plugins, on the other hand, provide the assistant with ongoing capabilities that it can execute independently. This includes connecting to services like Gmail for inbox reading, managing calendars, tracking expenses, syncing notes, and monitoring code repositories.

Each plugin is designed to be a complete function that the assistant owns, rather than just a one-off prompt or instruction bundle. To develop plugins, Vellum uses TypeScript packages with lifecycle hooks, which differ from skills in their design and functionality. The manifest for each plugin utilizes the vellumai plugin-api and exposes specific hooks for initialization, user prompts, tool usage, model calls, and shutdown, ensuring that state is persisted to a local JSONL file.

This setup allows more people than just infrastructure engineers to build plugins, making it easier for users to create custom integrations without needing extensive coding knowledge. To cater to those who prefer not to write code, Vellum offers a plugin builder skill that generates a plugin from a plain-language description. This feature is crucial in democratizing the development process and enabling more users to contribute to the Plugin Hub.

Another significant aspect of this setup is credential handling. When plugins require API keys or OAuth tokens for accessing email, banking, or code repositories, those credentials remain on the machine and are not sent to the model. This ensures that sensitive integrations can be trusted with the assistant’s capabilities. Plugins themselves are open-source and portable, allowing them to be shared among users rather than being tied to a single vendor.

A notable feature of plugins is their ability to operate across different platforms. A plugin installed on macOS can run through Slack or Telegram with shared memory, making its capability available wherever the assistant is accessed. This flexibility enables developers to build once and deploy multiple times, streamlining the development process for users who need cross-platform compatibility.

The launch catalog includes a variety of use cases that demonstrate the potential of plugins in real-world applications. The Marketing Expert plugin bundles positioning, launches, content creation, brand voice management, and reporting into one cohesive tool. Similarly, the AI Hero Engineer Kit packages engineering workflows around plans, PRDs, vertical-slice issues, and test-driven loops.

Other notable examples include the Writing Coach, which runs daily prompts that escalate with a streak and critiques drafts, as well as the Fitness Companion, which logs meals, workouts, and weight, pulls nutrition data from OpenFoodFacts, and calculates macro targets. These plugins showcase the versatility of Vellum’s ecosystem in handling diverse tasks and workflows.

Users can install plugins directly from the catalog or by providing a GitHub URL for direct download. Authors are also encouraged to publish their own plugins to the same catalog, fostering a community-driven approach to plugin development and sharing. This collaborative environment is expected to drive innovation within Vellum’s ecosystem, as users contribute and build upon existing capabilities.

The Plugin Hub marks an important step in transforming Vellum from a product into a platform that can be extended and customized by its users. With this launch, the company aims to provide more comprehensive support for data analysis tools and AI assistants in various business settings.

Microsoft’s latest push in AI assistants has reached a new level of maturity. The company is shifting its standard apps from chatbots that generate text to workflow producers, aiming to turn artificial intelligence into an autonomous digital workforce.

The idea behind ‘the agentic enterprise’ – Microsoft’s vision for the future of work – is to evolve AI systems beyond just answering questions. Instead, they will plan, execute, monitor, and collaborate on business tasks with minimal human intervention. It’s a significant upgrade in how companies use AI assistants today.

The first step toward this agentic enterprise is to transform Microsoft 365 Copilot from being largely reactive into an autonomous system that takes initiative. This means understanding goals rather than individual prompts, breaking work into multiple steps, and choosing the right tools for each task.

For example, instead of asking Copilot to write a sales report, users can say ‘Prepare next week’s executive sales review.’ The AI then gathers CRM data, analyzes spreadsheets, summarizes Teams conversations, identifies major customer risks, drafts PowerPoint slides, schedules presentations, and emails participants – all without constant prompting.

Microsoft 365 Copilot Cowork achieves this by turning massive, multi-step prompts into background execution plans with defined human checkpoints. It can run for hours or even days, making it a substantial leap forward in AI assistants for business use cases.

One of the most notable developments around Microsoft Copilot Cowork over the past few months is its transition from an experimental ‘Frontier’ feature to a generally available enterprise product. The company sees it as the next stage of AI assistants – one that doesn’t just answer questions but executes multi-step work on behalf of users.

To support this push toward more advanced AI tools, Microsoft has introduced two new research tools: Critique and Council. These enable more comprehensive data analysis and comparison across multiple top-tier models simultaneously, part of the company’s broader effort to develop multi-model AI capabilities.

Critique is designed for complex tasks like competitive intelligence, market analysis, scientific literature reviews, financial research, legal and policy analysis, and enterprise strategy documents. It requires evaluating evidence from many sources rather than just answering a single question – which makes it ideal for these types of high-level work.

Council works by routing the request to several models and presenting their responses side-by-side for comparison. This allows users to compare different answers to the same prompt and choose the one that best fits their needs, giving them more control over AI decision-making processes.

What’s unique about Council is its ability to query multiple AI models independently answer a single prompt. Users can see where models agree or disagree – which helps in evaluating the credibility of each response. In contrast, Critique involves having one model review and improve another model’s response before it’s returned.

In addition to these tools, Microsoft has released Legal Agent within Word as part of its 365 Copilot capabilities. This is specifically designed for legal professionals who need help drafting, reviewing, and analyzing documents directly from the comfort of their familiar document management environment – where they can interact with AI agents using natural language prompts.

Legal Agent assists throughout a legal document’s lifecycle by combining generative AI with enterprise document management features like citations, organizational knowledge, and version control. It drafts contracts based on templates or user input, reviews agreements for unusual clauses or inconsistencies, compares document versions to highlight changes between drafts – and summarizes lengthy contracts into concise overviews of key terms, dates, parties involved, and risks.

It can also suggest revisions to improve clarity or align language with organizational standards. Furthermore, Legal Agent extracts important terms from documents like renewal dates, payment obligations, termination provisions, indemnification clauses, and governing law – and even generates executive summaries that non-lawyers can quickly understand without needing extensive legal background.

Because Legal Agent operates within Word, lawyers interact with the agent using natural language prompts while continuing to edit their documents normally. Microsoft emphasizes that Legal Agent is intended as a tool for attorneys, not a replacement – users still need to review AI-generated content and provide approval before it’s finalized.

GPT-5.6, the latest flagship model series from OpenAI, has become the preferred model in Microsoft 365 Copilot, a suite of productivity tools used by millions worldwide. This update brings advanced AI capabilities to users’ daily workstreams, enabling them to create, analyze, and collaborate more efficiently across various applications.

The integration of GPT-5.6 into Microsoft 365 Copilot is expected to enhance the overall user experience, particularly in Word, Excel, PowerPoint, Chat, and Cowork. For instance, in Word, users will be able to draft, edit, and refine documents with fewer rounds of prompting, thanks to the model’s improved performance.

In Excel, GPT-5.6 will support deeper analysis while using tokens more efficiently, allowing users to move faster from data to insights. This is particularly relevant for businesses that rely heavily on data-driven decision-making. The integration also extends to PowerPoint, where users can turn early ideas into polished presentations with less manual guidance.

Cowork, a tool designed for cross-functional work and collaboration, will benefit from GPT-5.6’s ability to help users complete complex tasks and produce higher-quality outputs with minimal manual coordination. This is expected to streamline workflows and improve productivity across teams.

Nitin Agrawal, President of Copilot & Agents Core at Microsoft, expressed excitement about the partnership, stating that customers will be able to work more effectively with AI in their daily tools. He emphasized the potential for users to produce polished outputs in various applications, including Word, Excel, PowerPoint, and Cowork.

The integration is made possible through OpenAI’s API, which allows Microsoft to access GPT-5.6 directly. This partnership marks a significant step towards bringing advanced AI capabilities to more individuals and organizations worldwide. Nikunj Handa, Head of API Product at OpenAI, noted that the collaboration aims to help businesses get more value from AI in their existing tools.

The update is part of Microsoft’s ongoing efforts to leverage AI for business productivity. By integrating GPT-5.6 into its Copilot suite, the company is providing users with a comprehensive set of data analysis tools and AI capabilities that can be easily integrated into daily workflows.

OpenAI has introduced ChatGPT Work, a new enterprise offering designed to automate workplace tasks across various applications and files. This move marks the latest development in the intensifying competition among artificial intelligence (AI) providers.

The platform connects with popular apps like Slack, Microsoft Teams, Google Drive, and Sharepoint, as well as email systems, calendars, CRM platforms, project trackers, and other tools through plugins. ChatGPT Work is powered by OpenAI’s advanced AI model, GPT-5.6, which enables it to gather context from these sources.

Once connected, ChatGPT can perform a range of tasks, including creating finished documents, spreadsheets, presentations, reports, and websites. It can also analyze data while refining drafts in the background without requiring user intervention.

The company has emphasized that ChatGPT Work’s capabilities extend beyond individual users to support team collaboration. Scheduled Tasks allow projects to continue working on their own even when the user is not actively engaged, updating documents or slides as needed and circulating changes among team members.

ChatGPT Work will be available for Pro, Enterprise, and Edu plans starting Thursday and will roll out to Plus and Business plans in the coming days. It can also be accessed through the ChatGPT desktop app on every plan, including the free option.

The introduction of ChatGPT Work comes as OpenAI continues its research into agentic AI tools following the launch of Operator and deep research, which were later consolidated into ChatGPT Agent for individual users and Workspace Agents for automating enterprise workflows. This move is part of an ongoing effort to enhance the capabilities of its chatbot technology.

OpenAI’s latest offering joins a growing list of similar products from competitors like Anthropic, which released Claude Cowork – an agentic tool capable of planning and carrying out multi-step tasks autonomously. The development has sparked concerns about potential disruptions in industries such as data analysis, with some companies already seeing the impact on their stock prices.

Microsoft, OpenAI’s backer, also unveiled Copilot Cowork to expand its own agentic AI offering following Anthropic’s rollout. This intensifying competition among AI providers is set to continue as each company strives to develop more comprehensive and efficient tools for automating workplace tasks.

Custom Power Apps builds are now putting Microsoft’s Copilot and AI agents to work inside the systems teams already use, not in a standalone chatbot. This marks a significant shift from having these tools operate independently to being integrated directly into existing workflows. The integration is designed to make it easier for businesses to leverage the capabilities of Copilot and AI agents without requiring users to switch between multiple applications or interfaces.

According to Russell Kommer, founder and CEO of eSoftware Associates, which has been building custom Power Apps and Microsoft 365 systems since 2006, ‘Fix your permissions and data model before you buy a single extra license, and that groundwork is what makes Copilot deliver.’ This emphasis on preparation highlights the importance of having a solid foundation in place before implementing these tools. Kommer’s company has completed over 200 migrations with zero data loss, demonstrating their expertise in this area.

The difference between an AI assistant and an agentic AI agent lies in its ability to take action within a live system. While assistants can provide answers and suggestions, agents can read records, trigger workflows, update fields, and stay within user permission boundaries. This distinction is crucial for businesses looking to automate processes and streamline their operations.

Inside Power Apps, the agent can interact with Dataverse records, power Automate workflows, and update relevant fields while adhering to user permissions. This level of integration enables agents to perform real work, such as drafting next steps or updating systems when tasks are completed. The move from assistant to agent has become a clear change in how enterprise software is built this year.

Agentic AI is moving from being a rare feature to a standard expectation in enterprise software. However, many organizations struggle with scaling their AI initiatives and measuring the impact on profitability. A key challenge lies in implementing robust risk controls and governance frameworks that allow agents to safely interact with live business data.

Copilot stalls without a solid data foundation. It can read whatever a user can already open, making loose permissions a live exposure as soon as it’s switched on. If deployed over a messy permission model, Copilot can surface sensitive records to people who were never meant to see them. This highlights the need for companies to clean up their Microsoft 365 permissions and data governance before implementing these tools.

The broader pattern across the market is that many organizations have adopted AI but struggle with scaling it. Fewer still can point to a measurable profit impact. An AI readiness assessment helps fix the data and permissions before deployment, moving projects out of this holding pattern so the AI is useful and safe from day one.

Governance separates pilots from production agents. When agentic projects get canceled, the cause is usually a missing set of rules rather than broken technology. Many projects are abandoned due to rising costs, unclear value, and weak risk controls rather than by the models themselves.

The market is crowded with AI tools that can summarize, draft, and chat. However, building an agent that safely interacts with live business data while following permissions and leaving an audit trail after every action is a more challenging task. These controls are what move an agent from being a tested prototype to one that’s ready for production.

Kommer added, ‘We scope the permissions and the audit trail before we scope the agent, because a confident wrong answer in front of the wrong person is what gets a project shut down.’ This emphasis on governance highlights its critical role in ensuring agents operate safely within live systems.

For most companies, the technology to implement Copilot tools already exists. What decides the outcome now is whether their data and permissions are ready to carry real work. This part of the equation remains fully under a company’s control.

Frequently Asked Questions about Custom Power Apps reveal that running an AI readiness assessment before turning on Copilot can help determine if the environment is prepared for these tools. The assessment checks licensing, data classification, permissions, and governance since Copilot can reach anything the signed-in user already accesses.

Power Apps supports custom business systems such as CRMs, ticketing, and case management. Dataverse handles relational data while Power Automate manages workflows, inheriting Microsoft 365 security and identity in the process. This integration enables a full process like HR onboarding to be automated from start to finish using a combination of Power Apps front end, Power Automate, and an embedded agent.

Copilot and AI agents can indeed be deployed securely within compliance-sensitive businesses when they run on a governed foundation. Role-based access, data classification, and Microsoft 365 compliance controls keep the agent within set boundaries so it acts only on data cleared for the signed-in user to see.

The use of AI coding assistants is transforming software development, but many companies are still focused on the wrong question. Instead of wondering which AI model is best, they should be asking how much project context their AI has. A recent benchmark shared by a developer on X illustrates this point. The developer reported that an AI coding model performed significantly better on Convex application development tasks when it was given a structured guidelines file. Without that file, performance declined.

The broader lesson here is not about one model or one development platform. It’s about how AI coding assistants work. They perform better when they are given durable, project-specific instructions rather than a vague prompt and a blank screen. This is especially important for supply chain software, which operates inside complex enterprise environments with specialized workflows and integration logic.

Supply chain software involves transportation management systems, warehouse management systems, supply chain planning platforms, order management systems, visibility platforms, and ERP-connected applications. These systems require human developers to learn the rules of each project before they can start coding. An AI assistant faces the same challenge without context files. Without them, the model has to infer too much from vague prompts, which may lead to incorrect code generation.

Early AI-assisted development relied heavily on prompt engineering. Developers repeatedly explained the same requirements: tech stack, coding conventions, data model, API design, security requirements, testing expectations, and documentation style. However, this approach does not scale well for large projects or teams. A better pattern is emerging: persistent project guidance.

Context files provide a reusable understanding of how the project should be built. They tell the AI which frameworks to use, how database tables and APIs are structured, which coding patterns are approved, which patterns should be avoided, how errors should be handled, how tests should be written, how security and permissions should be implemented, and how documentation should be formatted.

The value of context becomes even clearer in supply chain technology. A transportation management system must reflect the operations of shippers, carriers, brokers, forwarders, warehouses, and customers. It requires a deep understanding of receiving, putaway, picking, packing, replenishment, cycle counting, labor constraints, automation interfaces, and inventory accuracy.

A warehouse management system needs to understand how goods are received, stored, picked, packed, shipped, and tracked. A planning application must account for demand signals, supply constraints, lead times, service levels, capacity, inventory policies, and scenario analysis. An AI coding assistant that lacks this context may still generate syntactically correct code but will not be operationally useful.

Persistent guidance can improve more than just code quality. It helps teams reduce rework during code review, maintain consistency across modules, onboard new developers faster, improve test coverage, generate better documentation, lower AI usage costs by reducing corrective prompts, and preserve architectural discipline as teams scale AI adoption.

This is especially important for companies moving beyond experimentation with AI in production development workflows. The more AI is used, the more governance matters. Context files are not a substitute for engineering discipline but can reduce ambiguity and make it more likely that generated code conforms to how the enterprise actually builds and runs software.

The next phase of AI-assisted software development will be defined by how well companies capture and reuse their own institutional knowledge. For supply chain software vendors, logistics service providers, manufacturers, retailers, and industrial companies, the lesson is clear: AI coding assistants need more than prompts; they need context files to provide a reusable understanding of project requirements.

The use of AI in supply chain operations is shifting from capability to execution, where context, governance, workflows, thresholds, and action pathways determine whether AI improves real decisions across planning, logistics, sourcing, fulfillment, and risk management. This requires companies to build strong project guidance into their AI development workflows for better code quality, faster delivery, lower rework, and more consistent enterprise software outcomes.

AI coding assistants are not just tools for generating code; they can also improve data analysis and visualization capabilities in supply chain operations. By providing a reusable understanding of project requirements, context files enable teams to reduce the time spent on corrective prompts and improve test coverage, leading to better decision-making across planning, logistics, sourcing, fulfillment, and risk management.

The use of AI tools for business is becoming more widespread, but companies need to focus on how they can leverage these tools effectively. This requires a shift from relying solely on prompt engineering to using persistent project guidance with context files that provide a reusable understanding of project requirements.

A major development in the digital experience landscape has been announced by Oshyn: a Model Context Protocol (MCP) server that brings no-cost Digital Experience Platform (DXP) strategy tools directly to popular AI assistants. This integration enables marketers, digital leaders, and developers to access Oshyn’s 25 years of implementation expertise within their daily-used AI environments.

CYPRESS, Calif., July 7, 2026 - The MCP server is a significant release for Oshyn, allowing users to query its extensive strategic resources using plain-language conversational prompts. This means that digital leaders can evaluate sites, estimate budgets, and plan platform migrations entirely through natural conversation with AI assistants like Claude, ChatGPT, and Cursor.

The integration of Oshyn’s tools into a single MCP server is made possible by the Model Context Protocol, an open standard enabling AI models to securely connect to external tools and live data. With this new release, AI assistants can actively execute tasks and retrieve real-time insights rather than relying on static, generalized training data.

Oshyn has also integrated its proprietary Discoverability Assessment into the MCP server. This tool was previously reserved exclusively for prospective clients working directly with Oshyn’s advisory team but is now available to anyone through public self-service access. The assessment evaluates a brand from an AI’s perspective, helping companies understand how their messaging is interpreted by discovery engines and how they compare to industry rivals.

The Discoverability Assessment can be triggered by an AI assistant using conversational prompts like ‘Run a discoverability assessment for acme.com.’ This tool provides valuable insights into the effectiveness of a company’s digital presence. Other capabilities now accessible via AI prompt include Site Reliability Reports, DXP MatchMaker, Budget Estimator, and Knowledge & Data Retrieval.

Site Reliability Reports instantly scan a website for performance, discoverability, accessibility, and security issues using conversational prompts like ‘Analyze my website, acme.com, and email the report to me at [email protected].’ The DXP MatchMaker guides users through a questionnaire to narrow down a shortlist of appropriate digital experience platforms tailored to specific organizational needs. Budget Estimator generates realistic budget requirements for upcoming implementations or migrations using conversational prompts like ‘Estimate the budget for a Sitecore-to-headless migration with 300 pages.’

Knowledge & Data Retrieval allows access to Oshyn’s deep library of DXP content and proprietary industry metrics, such as Digital Trust Index scores. Users can retrieve this information using conversational prompts like ‘Show me Digital Trust Index scores for the financial services sector since January 2026.’ The MCP server has been extensively tested and works natively with major AI assistants and IDEs.

For detailed installation instructions, users should visit Oshyn’s website at www.oshyn.com/blog/oshyn-mcp-server. This release marks a significant milestone in the integration of AI tools for business, providing digital leaders with unprecedented access to DXP strategy tools through their daily-used AI environments. By leveraging this technology, companies can streamline their digital experience planning and execution processes.

Oshyn is a digital technology agency that specializes in helping marketing teams and their agencies build and maintain high-performance websites using market-leading platforms such as Adobe Experience Manager, Sitecore, Contentstack, and Optimizely. With 25 years of implementation expertise, Oshyn has earned a reputation for excellence while working with top-tier organizations across various industries.

Contact Patrick Wirz, VP of Marketing at [email protected] or 213-483-1770 for more information.

A new feature from Intezer is set to streamline the work of security teams by allowing them to automate custom security tasks. The company has announced Custom Agents, a capability that enables customers to build their own AI agents directly within the Intezer platform. This move builds on Intezer’s existing approach, which uses autonomous agents to handle security tasks and have humans oversee the process.

Security teams are no longer able to rely solely on manual alert handling or one-off automation due to the high volume and complexity of modern threats. Intezer’s core platform already employs autonomous agents to triage, investigate, and respond to alerts around the clock. These agents investigate 100 percent of alerts and escalate fewer than 2 percent for human review.

The introduction of Custom Agents allows customers to build on this foundation by automating additional investigation work, report generation, and other recurring SOC routines that are unique to their environment. This means security teams can now automate tasks such as writing custom incident reports, sharing shift handoff notes, tuning detection rules, and documenting investigation outcomes.

Intezer found through analysis of how teams used its AI chat for these workflows that more than a third of conversations involved the same repetitive tasks being requested again and again. Custom Agents turn those tasks into agents that run on their own, freeing up security personnel to focus on higher-level tasks.

The existing Intezer customers are already using agents in production for various tasks such as writing custom incident reports, providing recommendations on tuning rules based on triage verdicts, and proactive threat hunting. With Custom Agents, security teams can automate their unique individual and team processes by building their own AI agents that run on the same engine operating their SOC.

According to Itai Tevet, CEO of Intezer, ‘With Custom Agents, we are giving customers the power to automate their own unique workflows, running them precisely how they choose.’ This means security teams can describe what they want done, choose when it runs (on a schedule, on an event such as a closed case, or on demand), and pick the tools it can use.

Custom Agents work across the stack, combining Intezer’s built-in toolset with connected SIEM, EDR, and identity tools including CrowdStrike, SentinelOne, Splunk, Microsoft Sentinel, and Entra ID. They take action by updating, commenting on, and closing cases and emailing finished reports.

CyberProof Inc., a co-managed security services company, has introduced an agentic artificial intelligence service designed to automate up to two-thirds of security investigations. The new service, called CyberProof Agentic MXDR, pairs AI agents with human security experts across the detection, response, and exposure management lifecycle.

The service replaces manual security operations center workflows with a collaborative ecosystem of agents that work under human governance. This approach allows for faster alert triage and more accurate threat identification, which can lead to sharper investigation accuracy by as much as 30%. Unlike standalone AI tools or closed AI SOC platforms, CyberProof’s framework works seamlessly with models from various third-party vendors, including Microsoft Corp., Google LLC, Anthropic PBC, and others.

CyberProof Agentic MXDR is designed for large enterprises and mid-market organizations running complex hybrid cloud environments. The service includes a built-in quality control framework that continuously scores AI agents on effectiveness, speed, and cost. This allows chief information security officers to monitor accuracy versus compute spend as frontier model prices rise. CyberProof argues that its agentic approach can help bridge the gap between calendar-driven defense and the rapid pace of threat disclosures.

The company claims that generative AI has compressed the time attackers need to exploit new flaws, forcing organizations to operationalize threat intelligence faster than ever before. In a statement, Chief Executive Tony Velleca noted that collecting threat data is no longer a competitive advantage; instead, it’s about execution, reducing exposure, immediate detection, and containment. Legacy security operations metrics are becoming obsolete due to the speed gap between attackers and defenders.

CyberProof vice president Doron Davidson emphasized that the blend of AI agents and human expertise aims to deliver faster, more consistent investigations with fewer false positives. The 24/7 service reports outcomes through CyberProof’s Reveal360 dashboard and is available immediately for organizations weighing their operational maturity and stack compatibility. A complimentary readiness assessment is being offered to help teams evaluate their preparedness.

CyberProof delivers co-managed security operations built around Microsoft Azure and the wider Microsoft security stack, which it has been using since its founding in 2017. The company’s new agentic MXDR service is designed to streamline security investigations while maintaining human oversight and control. CyberProof Agentic MXDR can help organizations automate up to two-thirds of their security investigations, freeing up resources for more critical tasks.

The launch of CyberProof Agentic MXDR marks a significant step forward in the development of AI-powered security solutions. By combining the strengths of both humans and machines, this service has the potential to revolutionize the way organizations approach threat detection and response. As the cybersecurity landscape continues to evolve at an unprecedented pace, it’s essential for companies like CyberProof to innovate and adapt their strategies to stay ahead of emerging threats.

CyberProof Agentic MXDR is available immediately, with a complimentary readiness assessment offered to help teams evaluate their preparedness. The service reports outcomes through CyberProof’s Reveal360 dashboard, providing real-time insights into security operations. With its agentic approach and built-in quality control framework, this new service has the potential to transform the way organizations manage their security investigations.

CyberProof Agentic MXDR is designed for large enterprises and mid-market organizations running complex hybrid cloud environments. The service can help bridge the gap between calendar-driven defense and the rapid pace of threat disclosures. By automating up to two-thirds of security investigations, this new service has the potential to free up resources for more critical tasks and improve overall investigation accuracy.

CyberProof Agentic MXDR is a significant development in the field of AI-powered security solutions. The company’s agentic approach combines human expertise with machine learning capabilities to deliver faster, more consistent investigations with fewer false positives. With its built-in quality control framework and seamless integration with various third-party vendors, this new service has the potential to revolutionize the way organizations manage their security operations.

Enterprise organizations often struggle with document-heavy processes that consume significant time and resources. The main challenge is extracting valuable information from documents, which are frequently trapped in PDFs, forms, and other formats. This problem persists despite advancements in automation technologies like AI. To address this issue, Nitro has developed a platform called Nitro Automate, designed to bridge the gap between documents and automation workflows.

Built for high-volume document processing, Nitro Automate is an intelligent document automation platform that integrates document processing, workflow automation, and AI-powered integrations. This solution enables teams to eliminate manual document tasks and automate business processes more efficiently. With its fast deployment capabilities and ability to work anywhere PDFs are handled, Nitro Automate can start delivering value almost immediately.

Nitro Automate extends the company’s existing document productivity solutions – Nitro PDF, Nitro Sign, and Nitro Smart Redact – by addressing the challenge of managing document operations across AI agents, workflows, and systems. Instead of relying on employees to manually move documents between systems or extract data from forms and documents, Nitro Automate automates tasks throughout the document lifecycle.

The platform’s capabilities include process automation, conversion, reshaping, transformation, compression, security, and integration with enterprise applications. It works at two levels: team and department level, where it combines document automation, AI-powered data extraction, and workflow orchestration to eliminate manual document work for high-volume processes; and individual level, where it accelerates essential document tasks that still require human judgment.

Across both levels, Nitro Automate’s enterprise integrations make document data accessible to every stakeholder. This enables teams to automate a process or work through it directly. The platform transforms document-intensive workflows in four key ways: eliminating many manual PDF tasks, automating eSignature workflows beyond the signature, unlocking data trapped in documents, and building document workflows that work in the AI era.

One of the primary challenges Nitro Automate addresses is the substantial operational overhead created by repetitive manual document steps. These activities may seem insignificant when viewed individually but can add up to hours of low-value manual work embedded inside otherwise automated workflows. By removing these manual document steps from core business workflows, teams can focus on higher-value work while improving process consistency and reducing errors.

Nitro Automate also helps organizations automate eSignature workflows beyond the signature itself. This involves integrating document preparation, routing, approvals, and Nitro Sign workflows into a single automated experience. Instead of managing signatures manually across disconnected tools, teams can create workflows that automatically move documents through each stage of the lifecycle, resulting in faster turnaround times, improved visibility, and fewer bottlenecks.

Another key capability of Nitro Automate is its ability to transform unstructured document content into actionable business data. Critical information stored inside contracts, invoices, applications, forms, and other documents can be accessed using AI-powered tools like Nitro Automate. This enables downstream systems and workflows to accelerate processes such as invoice and accounts payable automation, employee onboarding, contract management, customer intake, compliance reporting, claims, and case management.

As enterprises move beyond AI copilots that assist with individual tasks towards deploying AI agents capable of executing multi-step workflows autonomously, document automation becomes an increasingly important part of AI strategy. Nitro Automate addresses this challenge by providing flexible integration options supporting both human- and AI-driven workflows. Business teams can build their own automations using low-code and no-code tools, while developers can easily integrate Nitro Automate into existing applications and workflows using APIs.

Nitro Automate is compatible with the Model Context Protocol (MCP), an emerging open source standard that allows AI agents to securely access external tools and services. Through MCP, AI agents can use Nitro’s document automation solution to interact with documents inside business workflows, making Nitro Automate a document execution layer for enterprise AI initiatives.

Organizations investing in automation and AI should recognize the significant opportunity for operational improvement presented by document-intensive processes. By bridging the gap between documents, workflows, enterprise systems, and AI agents, Nitro Automate brings together document processing, eSignature automation, data extraction, and workflow orchestration to create intelligent workflows that can scale alongside next-generation enterprise AI.

Document-heavy processes are often a major bottleneck in business operations. Nitro Automate helps organizations eliminate these bottlenecks by automating tasks such as manual PDF management, signature routing, and document approval. By doing so, teams can improve efficiency, reduce errors, and free up resources for higher-value work.

Docusign has announced a new integration that enables in-house legal teams to automate contract workflows across their entire business using artificial intelligence. The company’s Intelligent Agreement Management (IAM) platform is now available for use with Perplexity Computer and its associated tool, Computer for Counsel. This partnership aims to streamline the often laborious process of drafting, reviewing, signing, and managing contracts by leveraging AI-powered automation tools.

The integration uses Docusign’s Model Context Protocol (MCP) server to connect with Perplexity, allowing legal teams to set objectives using plain language. In response, Docusign can automate contract work from start to finish, reducing the time spent on manual tasks and freeing up resources for more strategic work. This collaboration is particularly beneficial in large enterprises where multiple teams are involved in contracting processes.

Allan Thygesen, CEO of Docusign, emphasized that contracts form a critical part of how businesses operate, with legal teams often stuck juggling multiple tools to manage contract information. By integrating Docusign’s agreement intelligence and workflows directly into the AI tools used by legal teams, the company aims to reduce document management time and enable faster business movement.

Nathan Barksdale, General Counsel at Perplexity, shared his experience with scattered contract work across various tools. He noted that connecting Docusign to Computer means legal teams can automate agreement workflows from end-to-end, allowing them to focus on strategic legal work rather than manual document management tasks.

The integration is powered by the Docusign MCP server and is available today in English globally. This move aligns with Docusign’s mission of bringing agreements to life for nearly 1.9 million customers across more than a billion people in over 180 countries. By leveraging AI-native tools, companies can create, commit, and manage agreements more efficiently.

Docusign has been recognized as one of TIME’s World Most Sustainable Companies 2026, highlighting its commitment to sustainability and business practices that minimize environmental impact.