PM AI Tool Use
(2026 Edition)
5 principles and 4 traps for PMs designing tool-using agents.
Build Tool-Use PM Skills — Free →5 Principles
Tool descriptions are the prompt — invest in clarity
Few well-chosen tools beat dozens
Validate inputs before tool execution
Handle tool failures gracefully — most tools fail occasionally
Log every tool call for debugging and eval
4 Traps
Too many tools — model confusion increases linearly
Vague tool descriptions — model picks wrong tool
No retry / fallback when tool fails
Ignoring cost and latency of tool calls
FAQ
Why is tool-use reliability the bottleneck for agents?
Because compounded error rates kill agent loops. If each tool call has a 95% success rate and an agent makes 10 calls, end-to-end success is ~60%. The math gets brutal fast. Agents that work in production minimise tool calls, validate inputs, and retry intelligently — not just 'hope the model does the right thing.'