The Hiring Debrief: From product manager to agent manager

Zakir Tyebjee published this article in June 2026 in The Hiring Debrief, a Substack newsletter focused on tech talent and organizational structure. It makes the case that managing AI agents is not a technical task but a management task — one PMs already understand, expressed in new terms.

The central argument is structural. Traditional product management involved coordinating human teams: writing specs for engineers, setting direction with designers, aligning stakeholders. In organizations that have adopted agentic AI, a growing portion of actual output is now produced by agents executing multi-step workflows. Tyebjee argues this does not require entirely new skills — it requires remapping existing management intuitions onto a different kind of worker.

The article offers a direct mapping between familiar PM practices and their agent-management equivalents. Hiring becomes speccing the agent. Onboarding becomes context engineering — writing system prompts that give the agent the background it needs to operate well. Weekly one-on-ones become evaluation loops, where output is reviewed against criteria rather than discussed. Performance reviews become capability expansion — updating the agent’s scope when it has earned trust. Coaching becomes feedback design, and decommissioning replaces firing.

Four skills emerge as essential in this framing. Context engineering means writing precise, well-structured system prompts; agents perform according to the clarity of the context they are given. Evaluation design means defining what good output looks like in measurable terms, since agents cannot be judged intuitively the way human colleagues can. Trust calibration means knowing which decisions require human review and which can be delegated fully. Hybrid org design means deciding how to route work between people and agents — a genuinely new operational question for product teams.

The article is particularly useful for PMs who are already overseeing AI-assisted workflows and want a framework for thinking about it systematically. It does not require deep technical knowledge of how LLMs work. What it requires is the same thing good management always required: clear expectations, defined success criteria, and deliberate decisions about when to hand off and when to stay involved.