AI agent development guide: architecting autonomous workflows

AI agents are moving past simple chat interfaces. Today, they perform multi-step workflows like database migration, custom reports generation, and automatic email responses. But keeping an autonomous agent from entering a loop requires rigid engineering rules.

The anatomy of a reliable agent

A durable agent needs a strict planning phase, a list of structured tools (defined via JSON schemas), and an execution loop that terminates if it reaches a budget ceiling. Giving an agent raw, unformatted API access is a recipe for system errors.

Evals: The only way to launch

Never release an agent without running regression tests. You must set up an evaluation runner that inputs diverse test prompts and matches outputs against strict accuracy and latency targets.