AI & Technical question
Decagon wants enterprises to control how agents change in production. Define the product requirements for versioning, approvals, rollout/rollback, evaluation gates, and auditability. Then describe the minimum API surface, core data model, and architecture choices you would align on with engineering to support safe agent updates at enterprise scale.
- Decagon
- AI & Technical
- Hard
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What this question tests
Whether you can translate an enterprise trust requirement (safe agent updates) into concrete product requirements and a realistic minimum architecture, working credibly with engineering on system design.
How to approach it
- Versioning: every agent configuration change creates an immutable version with a diff against the prior version, so enterprises can see exactly what changed.
- Approvals: define role-based approval gates, for example a designated reviewer must approve any change before it can roll to production traffic, with the approval itself logged.
- Rollout and rollback: support staged rollout to a percentage of traffic or a specific customer segment, with one-click rollback to the last known-good version if evaluation gates fail.
- Evaluation gates: require a defined regression suite to pass, built from prior labeled failures and core use cases, before any version can be promoted past the staged rollout.
- Minimum architecture: a version-controlled config store, an audit log service capturing every state transition with actor and timestamp, and a routing layer capable of directing traffic by version and percentage, which together support versioning, approvals, rollout, and auditability without requiring a full rebuild of the agent runtime.
What a strong answer includes
- Connects each product requirement to a concrete architectural component, showing the requirements are actually buildable, not just a wish list.
- Ties evaluation gates to a regression suite built from real prior failures, giving the gate real teeth instead of being a generic quality check.
- Names rollback specifically as one-click to last known-good, addressing enterprise control expectations directly rather than leaving recovery vague.
Common mistakes
- Lists the five requirements without connecting them to any concrete data model or system component.
- Skips defining who has approval authority, leaving governance vague despite that being the core ask.
Likely follow-up questions
- How would you handle a rollback that needs to happen mid-conversation for an in-progress session.
- What would you do if two customers need different approval workflows for the same underlying platform.
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More questions from Decagon
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture