Custom AI Agents with LangChain and LangGraph

Custom AI agents are not chat UIs with personality. They are software systems that plan multi-step work, use tools, and operate inside your product or operations stack with clear boundaries.

Why LangChain and LangGraph

LangChain helps compose prompts, retrievers, and tools. LangGraph adds durable control flow for branching, retries, human-in-the-loop checkpoints, and multi-agent collaboration — the difference between a prototype and a maintainable workflow.

Production checklist

1. Define the agent’s job and non-goals in writing
2. Limit tools to the minimum needed actions
3. Log traces, failures, and escalation rates
4. Add evaluation sets before launch and after each model change
5. Decide cloud API vs open-source LLM based on data sensitivity

Open-source LLM options

For teams that cannot send data to hosted APIs, we deploy or integrate open-source models with the same agent architecture, then measure quality against your evaluation set — not against Twitter demos.

If you need custom agents, task automation, or private LLM work, talk to Kokatta about an AI Engineering engagement.