4 Fault Tolerance Patterns Every AI Agent Needs in Production

Your AI agent handles 50 requests in development. Every one succeeds. You deploy to production and within 72 hours, the provider rate-limits you, a tool returns malformed JSON, and your agent enters an infinite retry loop that burns $200 before anyone notices. This is not a hypothetical. We built a multi-agent system that ran 14 teams of autonomous agents. Three of those teams crashed in the first week — not because the logic was wrong, but because nothing in the system knew how to fail gracefully. Here are 4 fault tolerance patterns we implemented to fix it. Each one uses production-tested code with LangGraph and LangChain. Pattern 1: Retry Policies With Exponential Backoff The simplest failure mode: a transient error. The API returns a 503, a database connection drops, a model provider hiccups. Most developers handle this with a bare try/except and a fixed retry. That creates thundering herds during outages. LangGraph has built-in retry policies that handle this correctly — exponenti