The original title is: "Ten common production failures in AI agent deployments and how to fix them"

Short summary

A practical breakdown of ten recurring production mistakes when deploying AI agents, backed by data from Datadog's 2026 State of AI Engineering report and Gartner predictions. Key issues include missing automated evaluations (47% rollback rate without evals vs 9% with), inadequate retry/backoff for rate limits (60% of production LLM errors), and lack of checkpointing for long multi-step workflows. Each mistake includes a concrete fix.

• •5% of production LLM calls returned errors in Feb 2026; 60% were rate-limit or timeout failures
• •Agents without automated evals had 47% rollback rate vs 9% with full eval coverage
• •Gartner predicts 40%+ agentic AI projects cancelled by end of 2027 due to engineering failures

Generated with AI, which can make mistakes.