Building Production-Ready AI Agents with LangChain
AI agents are having a moment. But most tutorials show you the happy path — a demo that works in a notebook but falls apart in production. This post covers what we've learned shipping AI agents for real clients.
What makes an agent "production-ready"?
A production agent needs to:
- Handle failures gracefully — LLM APIs go down. Tools fail. The agent must degrade without losing user data.
- Be observable — you need to know what the agent did and why.
- Be predictable — stochastic models need deterministic guardrails.
- Be fast enough — users won't wait 30 seconds for a response.
Architecture
We use a simple pattern: router → tools → composer.
- The router classifies intent and selects the appropriate tool chain.
- Tools are small, focused functions with retry logic and timeouts.
- The composer assembles the final response from tool outputs.
This separation makes each component independently testable.
Key lessons from production
1. Cache aggressively. Embeddings and classification results are expensive. Cache with Redis and a 1-hour TTL.
2. Stream responses. Don't make users wait for the full response. Stream tokens as they arrive.
3. Set tight timeouts. Each tool call should timeout at 10 seconds. The entire agent response should timeout at 30 seconds.
4. Log everything. Every agent run should produce a structured log with: input, tool calls, tool outputs, final response, latency, and cost.
5. Human handoff is not optional. Always build an escape hatch. When confidence is low, route to a human and say so clearly.
Conclusion
Building AI agents is still more engineering than magic. The models are powerful, but reliability comes from the infrastructure around them.