Hard

Design an Evaluation Platform for AI Agents System Design Interview

Design datasets, trace grading, simulation, regression detection, and release gates for non-deterministic tool-using agents.

Mock Interview with AI

1. Problem Statement

Design an evaluation platform that decides whether a new model, prompt, tool, or orchestration change is safe to release for a coding and research agent.

2. Architecture Discussion Map

Use this as one discussion aid, not a single correct answer. Your design should follow from the requirements, scale, and trade-offs you establish.

Rendering architecture diagram...

Mermaid Source (For AI Bots)

graph LR
    A["Design an Evaluation Platform for AI Agents"]
    A --> F1["Eval taxonomy, datasets, and task environments"]
    A --> F2["Trace collection and structured graders"]
    A --> F3["Non-determinism, statistical power, and infrastructure noise"]
    A --> F4["Safety/adversarial cases and human calibration"]
    A --> F5["CI release gates, observability, and feedback loops"]

3. Key Focus Areas

1
Eval taxonomy, datasets, and task environments
2
Trace collection and structured graders
3
Non-determinism, statistical power, and infrastructure noise
4
Safety/adversarial cases and human calibration
5
CI release gates, observability, and feedback loops

4. What Strong Candidates Should Demonstrate

✓Evaluate complete workflows rather than only final text.
✓Control non-determinism, infrastructure noise, and grader bias.
✓Turn production failures into repeatable datasets and release criteria.

Evaluation Guide

This is an evaluation framework, not a single model answer. Strong designs may make different choices when their assumptions and trade-offs are explicit.

What and how to measure

25%

Senior signals

✓Defines task success, tool selection, policy compliance, recovery, cost, latency, and final-answer quality.
✓Separates offline benchmarks, simulations, canaries, and production monitoring.

Trace and grader architecture

25%

Senior signals

✓Captures model calls, tool calls, handoffs, guardrails, environment state, and artifacts.
✓Uses deterministic checks where possible and calibrated model/human graders where necessary.

Experimental rigor

30%

Senior signals

✓Runs repeated trials, confidence intervals, stratification, paired comparisons, and environment controls.
✓Detects benchmark contamination, flaky tasks, grader drift, and infrastructure noise.

Release and learning loop

20%

Senior signals

✓Defines severity-weighted gates, rollback, shadow/canary traffic, and failure triage.
✓Converts production incidents and user feedback into versioned regression datasets.

Trade-offs to articulate

Realism versus repeatability.
Human judgment quality versus evaluation cost.
Strict gates versus iteration speed.

Practice follow-up questions

1.Success rate moves from 72% to 75%. Is that enough to ship?
2.The same model scores differently on two sandbox images. How do you investigate?

Want interactive feedback?

Practice drawing this system component-by-component on a live whiteboard while the interviewer probes at your target level.

Start Interview

Core Concepts

Agent EvalsTrace GradingDatasetsSimulationRelease Gates