AI & Technical question
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
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What this question tests
Tests designing a rigorous agent evaluation framework covering long-horizon task completion and failure recovery while avoiding overfitting to the current harness.
How to approach it
- Define task categories reflecting real enterprise use, such as multi-step workflows spanning several tools and sub-agents, not single-turn Q&A.
- Score on task completion, plus efficiency in steps taken, plus recovery, whether the agent notices and corrects a failed tool call.
- Build the eval to be harness-agnostic where possible, testing decisions and outputs rather than internal implementation details.
- Include held-out, unseen task variants refreshed regularly, since a static eval set gets gamed by tuning to its specific cases.
- Validate that eval scores correlate with real customer outcomes using a sample of production deployments as a sanity check.
What a strong answer includes
- Explicitly scores failure recovery as a separate dimension from raw completion rate, since silent failure is a real enterprise risk.
- Uses regularly refreshed, held-out tasks to guard against overfitting to the current harness.
- Ties eval design to production correlation, checking that a high score actually predicts a good live outcome.
- Notes the tension between fast, repeatable training-infrastructure needs and slower, more realistic product-harness testing, and designs for both.
Common mistakes
- Building an eval so tied to the current harness it can't detect genuine model-level improvements or regressions.
- Measuring only completion rate and missing efficiency or recovery, which matter more for long-horizon reliability.
Likely follow-up questions
- How would you refresh the eval set without invalidating historical comparisons?
- How would you know if the eval is overfit to the current harness?
More ai & technical questions
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- 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
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- 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