PM AI Agents
(2026 Edition)
5 design dimensions, 5 eval basics, 4 traps for building agentic products.
Build Agentic PM Skills — Free →5 Design Dimensions
Autonomy — how much decision-making does the agent own vs defer to the user?
Tool surface — which actions can it take? More tools = more capability and more risk
Context window — what history does it carry? Memory architecture is load-bearing
Guardrails — what must it never do? Hard stops matter more than soft ones
Observability — can you trace every decision? Without this, debugging is impossible
5 Eval Basics
Task completion rate — did it actually finish the job?
Trajectory quality — did it take reasonable steps, or thrash?
Human intervention rate — how often does a user have to correct it?
Cost per task — tokens and tool calls aren't free
Latency — agents that take 2 minutes feel broken to users
4 Traps
Shipping without evals — 'looks good on demos' is not a quality signal
Letting the agent loop indefinitely — hard step limits prevent runaway costs
Hiding errors behind optimistic UI — users must know when agent is unsure
Treating hallucination as rare — plan for it as a first-class failure mode
FAQ
What's different about PM-ing AI agents vs regular software?
Three things: non-determinism means the same input can produce different outputs, so specs give way to evals; failure modes are probabilistic rather than deterministic; and quality degrades silently as models change. You spend more time on evaluation infrastructure than on shipping features.