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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.

A practical walkthrough of using LangSmith Insights to understand real user behavior in production AI agents.

See how LangChain v1 handles structured outputs without requiring JSON in prompts, using schema-first, provider-native support for more reliable and typed responses.

LangChain just launched 1.0 along with LangGraph and major LangSmith updates, and Focused was on site for it all. Learn how these releases redefine enterprise agentic AI and why Focused, an early LangChain partner, continues to lead in building production-grade, reliable agent systems.

Discover 5 powerful LangChain features we rely on daily at Focused to build production-ready AI apps and how LangGraph ties them all together.

What does it actually take to build a working multi-agent architecture? Agustín shares lessons from his first implementation, covering agent structure, evals, and why your use case should shape the design

Learn how Focused uses LangSmith to debug, test, and scale LLM pipelines. Go beyond prompt engineering and design systems built for production.