Building Reliable AI Systems with LangChain
A guide to architectural patterns for building production-grade AI systems that handle failures gracefully.
Production AI needs to be reliable. Relying on a single API call is a recipe for failure. LangChain provides the tools to build robust pipelines, but you have to architect them for the "real world."
Retry and Fallback Logic
Always implement structured retry logic with exponential backoff. For critical pipelines, set up automatic model fallbacks; if your primary model (e.g., GPT-4) times out or returns a rate-limit error, have the system automatically retry with a secondary, faster model.
Graceful Degradation
Design your system so that it can operate in a "degraded mode." If your vector search is down, maybe the agent falls back to a basic keyword search. If the LLM is overloaded, maybe the agent provides a cached response instead of trying to generate a new, complex analysis.