Agentic Experience(AX): Designing Infrastructure Products for AI agents

Developer Experience defined the last two decades of platform competition: companies won or lost on time-to-first-API-call, docs quality, and SDK ergonomics. Agent Experience(AX) is the next frontier, and most infrastructure products are not ready.

When your primary user shifts from a human developer to an AI agent, product design assumptions quietly break. Docs optimized for human scanning waste an agent's context window. Error messages written for humans give agents nothing to recover with. API surfaces built for exploration become expensive, ambiguous tool catalogs. And permissioning, a supposed agent problem, is often one your access model already solves.

This talk draws on hands-on evaluations pointing coding agents and MCP clients at real provisioning, scaling, and configuration workflows across major cloud platforms. I will share a practical AX framework: discoverability, context economics, error design as prompt design, idempotency and retries, and evals as the new docs testing. You leave with a checklist for auditing your platform's AX and a clear view of which agentic gaps are real and which are hype.