How the interviewer opens it
“Design a service where customers manage behavioral rules for their AI agents. Customers should be able to create, edit, delete, draft, publish, and roll back rules, and an external agent renderer consumes the active configuration. Focus on the data model, APIs, and data flow rather than large-scale infrastructure.”
The prompt is vague on purpose. The first few minutes are yours: narrow it to the core features and agree on the numbers before you draw anything.
What to clarify first
- ownership and scale
- rule structure
- draft workflow
- publication and rollback
- traffic and read:write mix
- latency and propagation
- consistency versus availability
- renderer integration
- durability and history
- validation and exclusions
The answers stay with the interviewer. In a SysRound round, Alex answers them as an interviewer would.
Scope of a strong answer
Core requirements
- Create, edit, delete, and order rules in an agent's draft configuration
- Publish the complete draft as an immutable, active release
- Inspect release history and roll back to previously published content
- Let an external renderer retrieve a complete, versioned active configuration
Usually out of scope
- Implementing the renderer, agent runtime, or LLM
- Proving that natural-language rules produce correct agent behavior
- Approval workflows, scheduled publication, and multiple draft branches
- Multi-region writes and large-scale infrastructure design
What interviewers probe
- Draft identity, release history, and rollback semantics
- Mutable rules and immutable history need different representations. A seemingly simple rollback can overwrite ongoing work or make an old configuration look like an old delivery event.
- Atomic publication under concurrent edits and retries
- A release must represent one coherent draft revision, and the active pointer, audit trail, and retry response must agree even when editors race or a response is lost.
- The renderer's versioned consumption contract
- A durable publication and a renderer applying it are separate events. Polling responses can arrive out of order, and rollback content must still be treated as a newer configuration.
Common mistakes
- Spending most of the interview on queues, caches, or sharding instead of defining draft, release, and active-state semantics.
- Publishing individual rules independently when the renderer needs a coherent whole configuration.
- Overwriting published content in place, making history and rollback unreliable.
- Ignoring lost updates, stale draft bases, or retries after a successful but unacknowledged publication.
- Treating a successful publication as proof that the external renderer has applied it.
- Checking tenant authorization only on the main agent lookup while accepting foreign rule IDs or rollback targets.
The bar by level
- Mid-level (L4/E4)
- Defines concrete entities and CRUD APIs, separates drafts from immutable published snapshots, and explains create/edit/delete → publish → renderer flows. Uses a database transaction for publication, a basic concurrency check, and a workable rollback that preserves history.
- Senior (L5/E5)
- Specifies revision and active-release preconditions, retry-safe publication, atomic snapshot construction, tenant-scoped references, and precise behavior for rollback with an existing draft. Defines the renderer's polling, version ordering, failure behavior, and publication-versus-application contract without overbuilding infrastructure.