Building Reliable Agentic AI Systems

Martin Fowler's website hosts an article by Mike Bayer on constructing reliable agentic AI systems. The article describes patterns for orchestrating multiple LLM calls, including branching, looping, and error handling. It emphasizes the use of deterministic guardrails, such as input validation and output schemas, to reduce hallucination risks. The author recommends a state machine architecture where agent transitions are explicit and logged. Techniques like chain-of-thought verification, human-in-the-loop for high-stakes decisions, and robust exception handling are covered. The article also discusses metrics for reliability, such as task completion rate and mean time to failure, and suggests using telemetry to monitor agent behavior in production.