LangChain Agents in Production: Lessons Learned

LangChain agents look magical in demos — give an LLM some tools, watch it reason through a task. In production, they introduce latency, cost, and failure modes that demos never show.

The first lesson: constrain your agent. Instead of giving it 20 tools, give it 3–5 well-defined tools with clear descriptions and strict input schemas. Fewer tools means fewer wrong turns.

Always set max iteration limits. An agent that loops indefinitely will burn through your API budget and frustrate users. A limit of 5–10 iterations handles most real tasks.

Implement structured error handling at every tool call. When a tool fails, the agent should get a clear error message and either retry with different parameters or gracefully tell the user what went wrong.

Logging is your best friend. Log every agent step — tool calls, inputs, outputs, and token usage. When something goes wrong in production, you'll need this trail to debug.

For most production use cases, consider whether you actually need an agent. A well-designed chain with conditional routing often delivers the same result with better predictability and lower cost.