Model Context Protocol (MCP)
5 stories filed under Model Context Protocol (MCP) on Beyond Market Intelligence. The newest of them: “How LinkedIn built context-aware AI agents for massive codebases”, “Empower autonomous agents with a clear identity before they act”, and “Your Slack bot just got permission to read the whole room”. Ajay Prakash isn't just tinkering with AI agents; he's giving them a corporate memory. AI agents are entering the enterprise workforce, and they need their own identity before they ever reach a gateway. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every Model Context Protocol (MCP) story on Beyond Market Intelligence, newest first.
How LinkedIn built context-aware AI agents for massive codebases
Ajay Prakash isn't just tinkering with AI agents; he's giving them a corporate memory. At LinkedIn, he tackles a familiar pain point: large codebases that leave agents lost. His answer is Contextual Agent Playbooks and Tools, built on MCP, which feed agents procedural know-how and runbooks directly. The 20% productivity gain with zero reliability loss is a compelling argument for context done right. For more on enterprise AI adoption, check out our related piece on navigating ethical considerations.
Empower autonomous agents with a clear identity before they act
AI agents are entering the enterprise workforce, and they need their own identity before they ever reach a gateway. Authentication alone won't cut it anymore. Once an agent starts reasoning, invoking tools, and coordinating across systems, the real question shifts from "Who are you?" to "What are you actually doing?" Runtime trust is the new security mindset, continuously validating behavior during execution. It's a practical evolution, and for deeper groundwork, our guide to distributed training algorithms offers useful context on the underlying systems.
Your Slack bot just got permission to read the whole room
Claude Tag now reads the full Slack conversation, not just isolated messages. That small technical change carries a big idea: AI works best when it's part of the team, not a tool you visit. Anthropic calls this "multiplayer AI," and Scott White argues it's the shift from personal assistant to company chief of staff. It's a bet that context, not intelligence, is the real bottleneck. We're watching that thesis get tested in real time.
GM uses AI agents to free engineers from 85% of non-coding work
General Motors' autonomous driving division didn't just hand engineers a better coding tool. It redesigned entire workflows around AI agents, and the payoff is hard to argue with: three times as many merged pull requests, faster releases, and fewer defects slipping downstream. Rashed Haq's point is worth sitting with. Most engineering time was never spent writing code anyway. Automating the other 85 percent, the analysis, triage, and testing, is where the real transformation lives. That is a smarter way to think about AI's role.
Brex lets real agent behavior shape smarter AI security policies
Brex stopped trying to police AI agents with SDK-level rules and started watching what those agents actually do. Its open-source proxy, CrabTrap, sits between every agent and every network request, using an LLM-as-a-judge to make enforcement decisions in real time. The insight is simple: the transport layer is an underused enforcement point. By bootstrapping policies from observed traffic instead of writing them from scratch, Brex found that governance gets sharper as agents get more capable.