Where agents help product work
Agents are useful when they compress information: meeting action items, survey clusters, app review pain points, support themes, launch drafts, and competitor notes. They are weaker when asked to replace prioritization judgment.
- Use agents to prepare the decision, not make the whole decision.
- Keep source links and raw examples attached to summaries.
- Ask for uncertainty and counterexamples, not only confident conclusions.
Good first workflows
Start with feedback clustering, action-item extraction, release-note drafting, and support-ticket synthesis. These workflows create value while keeping a human product owner in control.
What to avoid
Do not let the agent invent customer evidence or cite non-existent metrics. Product decisions need traceability back to actual users, tasks, and business constraints.
Low-risk places to start
use-case guidance can usually start with drafting, tagging, summarization, routing, and internal notes. These steps create value while keeping humans in control of final commitments, customer-visible replies, and system writes.
- Use the agent as a recommender before it becomes an actor.
- Keep raw inputs and outputs for review.
- Measure human repair time, not only model score.
Where not to automate first
Refunds, compensation, legal obligations, account permissions, compliance claims, and angry escalations should not be fully automated until the evidence is strong. Start with evaluation, then draft mode, then limited automation.
Pre-launch checklist
Before using this use case in production, run a small retest with real inputs, edge cases, and a plan for what happens when the agent fails.
- Is there a clear human-review rule?
- Are model version and evaluation date recorded?
- Which outputs are not allowed to be sent or written automatically?
- Is there a fallback path when the agent fails?
A practical next step
If you are evaluating this use case, start with ten real samples: three normal cases, three edge cases, two high-risk cases, and two cases with strict language or formatting requirements. Run two or three candidate agents and compare quality, repair time, and critical failures.