Production

Pinterest has quietly become one of the first major consumer platforms to deploy the Model Context Protocol (MCP) at genuine production scale — not as a proof-of-concept or demo, but as live infrastructure that engineering teams use daily to automate complex internal tasks. The news, reported by InfoQ this week, is a significant data point for anyone betting on MCP as the standard interface layer for enterprise AI agent integration. ...

There’s a wall every engineering team hits when they try to move AI agents from demo to production: identity and access management. An agent needs credentials to do anything useful — database access, API keys, infrastructure permissions. But credentials that live inside an agent are credentials that can be leaked, stolen, or misused. Traditional IAM wasn’t designed for ephemeral, autonomous software actors. And so most production agent deployments end up making one of two bad choices: over-permissioned agents with broad access they don’t need, or under-permissioned agents so locked down they can’t do their jobs. ...

Ask any platform engineer why their team hasn’t shipped AI agents to production yet, and you’ll get a version of the same answer: identity, access control, and audit trails. The problems aren’t exotic — they’re the same IAM challenges that have governed every production system for the past two decades. But the agent runtime has made them acutely worse. Teleport’s answer is Beams, announced at KubeCon CloudNativeCon Europe 2026 and launching as an MVP on April 30. ...

Model upgrades are supposed to make things better. Claude 4.6 did — eventually — but not before breaking production agent integrations in ways that caught teams completely off guard. The chanl.ai post-mortem published yesterday is exactly the kind of real-world account that practitioners need to read before migrating, not after. The LiveKit Incident: What Actually Happened The most concrete example in the post-mortem involves LiveKit’s Claude integration (GitHub issue #4907). When LiveKit’s team upgraded to Claude 4.6, their entire pipeline broke almost immediately — within two hours of deployment. ...

The Model Context Protocol has had a remarkable year. What started as Anthropic’s attempt to standardize how AI models connect to external tools and data sources has become, almost by accident, the de-facto tool layer for the entire agentic AI ecosystem. Claude uses it. OpenAI-compatible agents use it. Builders across the industry are shipping MCP servers like it’s the new API endpoint. But if you’ve tried to run MCP seriously in production, you’ve bumped into the same set of friction points. Authentication is awkward. Streaming is limited. Discovering MCP servers requires manual configuration. Multi-agent handoffs lack proper task lifecycle semantics. And when things fail — network blips, agent restarts, timeout conditions — the retry behavior is undefined. ...

Most multi-agent tutorials stop at “here’s how to wire two agents together.” Production systems need more: structured message passing, durable state across restarts, and an audit trail you can debug when something goes wrong at 2am. This guide builds a Planner/Executor/Validator architecture with LangGraph that’s actually ready for production. Architecture Overview The system uses three specialized agents: Planner — Receives a task, decomposes it into steps, publishes to the message bus Executor — Consumes steps from the bus, executes them, publishes results Validator — Checks Executor outputs against criteria, flags failures, loops back to Planner if needed These agents communicate via a structured ACP-style message bus (Pydantic schemas), checkpoint state to SQLite via langgraph-checkpoint-sqlite, and log every message to JSONL for auditability. ...

GitHub Engineering Blog: Why Multi-Agent AI Workflows Fail in Production (and How to Fix Them) Most multi-agent AI systems fail. Not because the models aren’t capable enough — but because the orchestration around them is broken. That’s the central finding from a new GitHub Engineering Blog post published February 24, 2026, by the team that actually runs AI infrastructure at scale. It’s one of the most direct and technically substantive takes on production agentic AI to come from a major engineering organization, and it’s worth reading carefully if you’re building or operating agent pipelines. ...