Agentic evals: a practical guide to knowing whether your AI agent did the job — and will do it again
blaizedsouza · x · 2026-10-11
An article aimed at engineers shipping agents built on LLMs — systems that call tools, change records, and act on a customer's behalf over multiple steps. It covers how to design agentic evals that answer two questions: did the agent actually complete the job, and can it do so reliably again? The piece argues that multi-step, side-effecting agent systems need repeatable evaluation practices rather than one-off output checks.
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