Product Context

Product context is everything a strong PM knows before making a call: customer pain, strategy, constraints, prior decisions, rejected paths, current roadmap, and quality bar.

Without product context, AI agents optimize locally. They may write correct code for the wrong product reason. Notiky turns product context into reusable memory for humans and agents.

Start with the customer and the problem: who experiences the pain, what they are trying to do, and what a successful outcome looks like. Add the current goal, relevant research, accepted decisions, and constraints such as accessibility, permissions, or supported platforms.

Keep a clear boundary between evidence and interpretation. A support request is a source; the conclusion drawn from several requests is an insight. An approach the team has agreed to take is a decision. An idea still awaiting validation is a hypothesis. These distinctions prevent a suggestion from becoming an accidental requirement.

A task that says simplify signup gives an agent little guidance about trade-offs. Product context explains which users abandon the flow, which information is required, why a verification step exists, and how the team will measure improvement. That context helps the agent propose a change that preserves the actual constraints.

Include the relevant prior decision when an apparently unnecessary step has a purpose. Also identify what can change. A useful brief gives an agent room to solve the problem while making the limits of that freedom explicit.

Product context is maintained knowledge, not a larger prompt. Review it when customer evidence or priorities change, name the owner, and retire superseded guidance. Use a context pack to select the subset needed for a particular task instead of handing every agent the entire company archive.