Product Manager Agentic AI

• Define and own the product vision, strategy, and roadmap for an agentic AI product built from zero — problem discovery, architecture direction, MVP scoping, and scale-up.

• Translate ambiguous, open-ended problems into a phased agentic system design: which tasks are agent-driven vs. deterministic, where human-in-the-loop checkpoints belong, and how autonomy expands release over release.

• Partner with engineering on core architecture decisions — agent orchestration and planning layers, tool-calling/function-calling design, memory and state management, RAG and retrieval pipelines, and multi-agent coordination patterns.

• Own the evaluation framework for agent quality — task success rate, hallucination/error rate, latency, cost-per-task, and safety guardrails — and use it to drive prioritization, not just measure it after the fact.

• Define prompt, tool, and knowledge-source specifications working directly with applied AI/ML engineers; review agent behavior transcripts and failure cases as part of the regular product cycle.

• Run structured discovery with enterprise customers and internal stakeholders to identify high-value workflows for autonomous or semi-autonomous automation.

• Own the build-vs-buy and framework decisions in collaboration with engineering (agent frameworks, vector databases, LLM providers, orchestration layers) and stay current on the fast-moving agentic AI ecosystem.

• Define and track north-star and input metrics for the product — adoption, task completion, time-saved, cost-to-serve — and report outcomes to leadership.

• Own the end-to-end roadmap prioritization, sprint-level scoping with engineering, and release planning; write clear PRDs, user stories, and acceptance criteria for agent-based features.

• Represent the product externally — customer demos, RFP/technical proposal support, and partner/analyst conversations — with the technical depth to answer architecture-level questions directly.