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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.

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.

Learn why Focused chose LangChain as our framework for building agentic systems. We break down the developer-first principles and architectural benefits that make it the right choice for production-ready AI applications.

Customizing memory in LangGraph enhances LangChain agent conversations and UX. This tutorial covers deprecated types, migration to LangGraph persistence, simple checkpointers, custom implementations, persistent chat history, and optimization techniques for smarter LLM agents.

It's time to replace the deprecated “initialize_agent” API with first‑class LangGraph nodes so you can cut latency and gain graph‑level control.

Discover how to implement a scalable, secure federated Supergraph using Netflix DGS, the Apollo Router, and CI/CD automation to build a modern data mesh platform.

Focused Lead Software Engineer Sarah Kainec breaks down AI evaluations, eval-driven development, and how tools like LangSmith and LangGraph help teams test LLMs, reduce fragility, and build production-ready agentic systems.

A look at how evolving workplace rituals, from daily standups to agent-focused Lunch & Learns, are helping teams navigate the shift toward human and AI collaboration.

Tips for testing complex React components using state, context, and controlled inputs.

What parenting twins taught me about retros, pairing, and building adaptable teams.