RAG systems and agents fail in ways traditional software doesn't, and they fail in ways traditional product managers don't anticipate. Retrieval quietly degrades as the corpus grows. Agents complete 9 steps correctly and torch the user's trust on the 10th. Costs scale with usage in ways your pricing didn't plan for. Product-managing these systems means treating evaluation, guardrails, and failure UX as features the PM owns.
I built these systems
At WisOwl AI I designed and implemented an embedding-based semantic matching engine using FAISS and Supabase pgvector, and shipped autonomous recruiter agents that match supply and demand in the Indian hiring market in real time. The platform grew to 10,000+ organic jobseeker signups and 10+ recruitment-firm and startup customers. Every lesson below came from that system misbehaving in production at some point.
What an AI product manager does inside your team
- Scope the right architecture for the job. Plenty of "we need an agent" problems are a retrieval problem plus a well-designed workflow. Deciding that early saves a quarter.
- Make evaluation the spec. Golden datasets, retrieval metrics, and behavioral evals defined before the build, so you can measure whether it's good enough to ship.
- Design for the failure case. Confidence signals, escalation paths, human handoffs, and undo, so one bad generation doesn't become a churned account.
- Own the unit economics. Token costs, latency budgets, and caching strategy belong in the PRD, because at scale they decide whether the feature survives.
I also spent eight years at CaaStle running growth product across a ~$50M+ ARR portfolio, so the AI work always connects back to retention and revenue. Engagements run fractional (1–3 days a week) or as a scoped build alongside your engineers.