AI & Technical question
North engineering wants to move quickly on new harness capabilities, while Modeling needs proof that those design choices help rather than constrain model behavior. What operating process would you set up so harness proposals are validated with Modeling before implementation, evals are shared across both teams, and regressions can be diagnosed as model gaps versus scaffolding gaps?
- Cohere
- AI & Technical
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Tests designing a cross-team operating process between a product engineering team and a research modeling team so harness changes are validated, not just shipped.
How to approach it
- Define harness capability concretely, such as new tool-calling scaffolding, memory, or orchestration logic that shapes agent behavior.
- Set up a shared eval suite both teams trust, so a regression can be diagnosed as a model gap or scaffolding gap rather than argued about.
- Require harness proposals to run against that shared eval before implementation, not after, to catch regressions early.
- Create a lightweight review checkpoint where Modeling signs off on behavior-affecting proposals, without bottlenecking low-risk scaffolding changes.
- Set a regression triage process that checks the shared eval logs first to attribute the cause before assigning it to either team.
What a strong answer includes
- Proposes a genuinely shared eval suite, not two separate ones each team trusts internally, since diagnosis needs common ground truth.
- Distinguishes which harness changes need Modeling sign-off from which don't, avoiding a bottleneck for infra-only changes.
- Builds regression triage into the process explicitly, since model-gap versus scaffolding-gap disputes are exactly what causes friction.
- Names a concrete cadence, such as a weekly joint eval review, to keep the two teams synced.
Common mistakes
- Requiring Modeling sign-off on every harness change, which slows Engineering to a crawl.
- Having two separate eval suites that each team trusts, making regression attribution impossible to agree on.
Likely follow-up questions
- How would you handle a harness change that passes the shared eval but still gets pushback from Modeling?
- What would you do if the two teams still can't agree whether a regression is a model gap or scaffolding gap?
More ai & technical questions
- Enterprise customers report that North agents lose track of objectives on long-running tasks as context accumulates. How would you choose among progressive tool disclosure, context summarization/compaction, persistent filesystem offloading, and trajectory instrumentation, and what metrics would tell you those changes actually improved long-horizon performance?Cohere · AI & Technical · Hard
- You have two quarters to make North agents production-ready for long, multi-step enterprise workflows. What would you ship first in the MVP of the execution layer, tool orchestration, parallel execution, sub-agent delegation, sandboxed code execution, or failure recovery, and how would you justify the tradeoffs between capability, reliability, and security-first enterprise requirements?Cohere · AI & Technical · Hard
- Design an evaluation framework for North agents that measures enterprise task completion, long-horizon reliability, and failure recovery across tools and sub-agents, while remaining compatible with both the product harness and model training infrastructure. What would you include, how would you score it, and how would you avoid overfitting the evals to the current harness?Cohere · AI & Technical · Hard
- North can adopt parts of an external agent/orchestration framework or build them in-house. What decision criteria would you use, and how would compliance, auditability, multi-tenancy, restricted or air-gapped deployments, and vendor lock-in affect your recommendation?Cohere · AI & Technical · Hard
- Design North’s third-party integrations experience end to end: connector framework, APIs, SDKs, plugin model, docs, and review lifecycle. How would you optimize for fast time-to-first-integration for partners and customers while preserving enterprise-grade security, identity control, and governance?Cohere · AI & Technical · Hard
- North runs inside a customer’s own infrastructure and positions itself as security-first enterprise AI. How should that deployment model change your integration product decisions, for example connector execution model, credential handling, least-privilege permissions, auditability, tool access, and which partners or categories you support first?Cohere · AI & Technical · Hard
More questions from Cohere
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