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AI agents — systems that decide which steps to take and which tools to call — promise to automate work that was previously too varied for traditional automation. In practice, the difference between an impressive demo and a dependable business tool comes down to a handful of engineering decisions.
Start with a narrow, well-defined job
Broad, open-ended agents are hard to test and harder to trust. Agents that do one workflow well — with clear inputs, tools, and a defined finish line — are easier to evaluate and improve.
Scope tools and permissions tightly
Every tool an agent can call is a capability it can misuse. Give agents the minimum permissions they need, separate read actions from write actions, and require confirmation for anything irreversible.
Make every run observable
- Record each step, tool call, input, and output
- Make runs replayable for debugging and evaluation
- Track cost and latency per run, not just in aggregate
- Alert on unusual behavior such as loops or repeated failures
Design the human handoff
Reliable agents know their limits. When confidence is low, inputs are ambiguous, or the stakes are high, the agent should pause and route the task to a person with the context they need to continue.
Evaluate on realistic scenarios
Test agents against a library of realistic tasks, including ones designed to tempt them into mistakes. Measure task completion, correctness, and policy adherence, and rerun these tests whenever tools, prompts, or models change.
