The PM’s priority is not to design prompts. It’s to own what the agent is supposed to do, how success is measured, and how autonomy expands safely.
Too many AI product managers fall into a trap: they become prompt engineers with a roadmap. They spend their time tuning model outputs instead of defining what “good” looks like for the business.
Define the contract, not the implementation
An agent has a job to do. The PM defines that job: what triggers it, what decisions it makes, what outputs it produces, and what metrics it’s held to. The implementation — which model, which orchestration framework, which retrieval pipeline — is engineering’s domain.
Own the autonomy ladder
The PM decides when an agent graduates from “draft only” to “execute inside hard limits.” That decision should be based on data: error rates, false positive rates, customer impact metrics. Not on vibes.
Build the eval framework
If you can’t evaluate your agent’s performance systematically, you’re flying blind. The PM should ensure that every agent has:
- Baseline metrics from the human-operated process
- Automated eval suites that run on every deployment
- Regression gates that prevent quality from degrading silently
This is not optional. This is the core product management work in an AI-native organization.