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Build multi-agent systems in LangGraph using supervisor and swarm patterns. Compare routing accuracy, latency, and real production tradeoffs, with implementation details and failure modes you’ll actually hit.

Build a RAG pipeline that embeds raw PDF bytes with Gemini Embedding 2. No text extraction for embedding, vision-model OCR for the text you need, and better retrieval than extract-then-embed

Our key takeaways from O11yDay NYC on observability, evals, and understanding your systems in production.

Build stateful AI agents with LangGraph memory persistence. Implement checkpointers, long-term memory stores, and cross-session state.

Stream AI agent responses in real-time with LangGraph. Implement low-latency streaming for enterprise applications with five stream modes.

Add human approval gates to LangGraph agents with interrupt() and Command(resume=...), plus the state management bugs that will haunt you at 2 AM.

Cut agent response times in half with parallel sub-agents in LangGraph, plus the failure modes that'll bite you in production.

Improper output handling is one of the most overlooked LLM security risks. This post demonstrates how prompt injection can generate executable payloads and how layered defenses prevent real-world exploits.

You already test. You open the browser, click around, verify the button works. That's a test—it's just trapped in your head. Now watch what happens when you build an AI agent: you tweak a prompt, squint at the output, decide if it's "good enough." That's an eval. The question isn't whether you do it. The question is whether you'll keep doing it by hand.

For the last 10 years, designers have been screaming "it's not about how things look, it's about how things work." The design community got the message. Nobody else did. And with agents, it doesn't matter, because there's no interface to design anymore. The forcing function is gone. What's left is ambiguity, partial information, and systems that need to infer intent from "Book me a flight to Detroit next Tuesday, afternoon, aisle seat." That's still a design problem. We just need a new word for it.

Focused has opened a London office to support enterprises moving AI agents from experimentation into real, production-grade systems. As organizations shift from demos to deployment, Focused is expanding into EMEA to help teams integrate, operate, and observe agentic systems inside the software they already rely on.

For fifty years, we designed interfaces to help humans talk to computers. Now AI agents can talk to computers for us. So what happens to design?

Most AI projects never make it past the demo phase. The gap between a flashy prototype and a production system that handles real enterprise workloads is vast—and that's exactly what LangChain was built to bridge. As one of LangChain's only boutique partners, we've spent years helping Fortune 500 companies and mid-market teams build agents that actually work in production. This isn't about another framework tutorial—it's a practical guide to deploying reliable, observable, and controllable AI agents using LangChain, LangGraph, and LangSmith. If you're stuck with POCs that won't scale or demos that can't handle the complexity of your real systems, this is your roadmap from prototype to production.

The question is no longer "can we build agents?" but "how do we integrate agents into everything we already have?" After years of proof-of-concepts and experiments, 2026 marks the year agentic AI moves from the lab to the enterprise—connecting autonomous agents to CRMs, ERPs, APIs, and decades of legacy systems. The companies that win won't have the most sophisticated models; they'll be the ones that solve the hard organizational problems of integration, governance, security, and testing for non-deterministic systems. The agent won't be the product. The integration will be.

Official partnerships. Production deployments. Knowledge transfer. These are what separate great LangChain agencies from the rest. We evaluated seven top consultancies so you don't have to start from scratch.

Static prompts don’t scale. This post breaks down a LangGraph pattern using tool calling for context engineering so agents pull only the context they need, when they need it.