Observe, eval, and deploy agents into the systems you’re already running
The hard part of agents is integration.
“Focused is the expert in the LangChain ecosystem, from deploying and operating LangSmith to building production-grade agents for complex enterprise environments.”
Harrison Chase
CEO, LangChain
Production-ready agents inside the systems you already run.
Production means real APIs, auth, business logic that lives in someone's head, and databases that were never designed to talk to agents. That's where LangGraph installations stall — and where Focused does its best work. Very few can make an agent work inside years of accumulated business software.
Architect your first agent
You have workflows that need agents, but nothing in production. We build a working agent on your real data, with the memory, integration, and orchestration patterns that make your tenth agent as straightforward as your second.
Harden your agent systems with LangSmith
Expand your working agent to a system that business operations can depend on. Build eval datasets from real data, configure LLM-as-judget and heuristic evaluators, and make sure regressions don’t reach production.
Partner on scaling agent systems
Your team takes over more each quarter while Focused provides hands-on guidance, monitoring behavior, tightening evals in LangSmith, controlling cost and extending the system into new workflows. You own the code, the data, and the infrastructure.
How Focused uses the LangChain stack
LangGraph
Architecture
LangSmith Evals and Observability
Deep Agents
Human-in-the-Loop
Reliable RAG
Not sure what you need?
Where teams bring us in
Customer
support agents
Support, service, and workflow automation across
customer-facing systems.
Research and
analysis agents
Agents that search, synthesize, compare, and surface evidence.
Internal operations agents
Long-horizon agents that plan and delegate to sub-agents - built when the problem needs it, not by default.
Voice
agents
Approval gates and interrupts wherever a person needs to stay in the decision.
Claims, compliance,
and review
Agents that extract, match, and draft for human review, with the eval framework that proves accuracy before it touches a real claim.
Knowledge and
retrieval systems
The hard part is retrieval quality. We select embedding
models, retrieval strategy and reranking logic based on
your data.
12+
Strategists at one of the world's largest PR firms were acting as human orchestration layers between disconnected data tools. Focused delivered 12+ production agents including a multi-agent deep research system that their teams now use daily, with evals gating every release.
~90%
OCS clients now get paid in an average of 32 days, far ahead of industry norms, on a claims platform that has processed 50,000+ invoices. Focused built the human-in-the-loop agent that reads submission packets, matches line items at ~90% extraction accuracy, and drafts justifications, so advocates justify differences instead of comparing PDFs.
Frequently Asked Questions
How do you handle observability and eval for LangChain agents in production?
We instrument every agent with LangSmith, with full trace visibility into every LLM call, tool invocation, and intermediate step. Before release, we build evaluation datasets from your real data, configure LLM-as-judge and heuristic evaluators, and integrate them into your CI pipeline so regressions don't reach production. Once live, LangSmith tracks latency, cost, and eval scores over time
Can LangSmith be self-hosted to keep sensitive data in our own VPC?
Yes. LangSmith supports fully self-hosted deployment on your own Kubernetes cluster in AWS, GCP, or Azure. Data never leaves your environment. We handle the infrastructure setup as part of an engagement.
How do we standardize agent development across teams and avoid rebuilding integrations from scratch?
We design shared LangGraph patterns that define how state is structured, how tools are defined, how integrations are built, so they're reusable across agents and teams. Every engagement ends with documented architecture and code your team owns. The second agent is faster than the first because the hard decisions are already made.
What's the right agent architecture for our use case?
It depends on the workflow, the systems involved, and where humans need to stay in the loop. A single LangGraph agent with tool use solves most first use cases. Multi-agent coordination makes sense when workflows are parallel, specialized, or too complex for one agent to handle reliably. Sometimes the right answer is a deterministic system, not an agent. We'll tell you which is which before writing a line of code.
How do we know when to use LangGraph?
LangGraph is LangChain's framework for building stateful, multi-step agent workflows. Use it when your workflow needs to branch, persist state between steps, coordinate multiple agents, or keep a human in the loop. If it's a straight line with no decisions, you probably don't need it.
How do we move from a working prototype to a production-hardened agent?
Most prototypes fail in production for the same reasons: no evaluation framework, integrations built against mock data instead of real systems, and no visibility into what the agent is doing once it's live. Making an agent production-ready means real integrations, LangSmith instrumentation, defined evaluators, and human-in-the-loop controls where your workflows require them.
What does a typical engagement look like?
Our engineers work with your teams, in your codebase. Depending on where you are in your AI journey, we can build your first agent and the right foundation to expand it. We can harden your existing agents, or we can partner to enable your teams to extend your agentic systems. Every engagement is scoped and timeboxed before we start.
How long does it take to get an agent into production?
It depends more on integration complexity than the agent itself. A well-scoped first agent with clear data access and defined workflows can ship in weeks. Hamlet went from start to market in three months. We scope every engagement upfront so there are no surprises on timeline or deliverables
