AI agent advocacy extends developer advocacy to machine users. It treats coding agents and other automated systems as participants that may discover documentation, evaluate tools, invoke integrations, and influence which products a developer adopts. The work includes making capabilities structured, current, testable, and easy for an agent to interpret.
The practice does not replace human communities or ordinary developer relations. People still establish credibility, explain intent, report nuanced problems, and decide which systems deserve trust. Effective agent advocacy combines machine-readable product information and measurable agent experience with transparent human support and feedback.
ELI5
Agent advocacy makes developer products easier for AI agents to discover, understand, test, and use. It provides structured, current information while preserving the human relationships that establish trust and explain intent.
For example, a software company can publish clear tool schemas, examples, limits, and test environments that coding agents can read. Human developers still need transparent support and a way to report nuanced problems or challenge product claims.
