Advanced~3h
Agent Evaluation: Harnesses, Metrics & Benchmarks in Agentic AI EngineeringInterview Readiness Check
Evaluating agents is fundamentally harder than evaluating single LLM calls. Trajectory evaluation vs outcome evaluation. Turn-level vs session-level metrics. Task success rate, tool call accuracy, over-refusal rate, loop efficiency (turns to completion), cost per successful task. Public benchmarks (SWE-Bench, WebArena, GAIA, AgentBench). Building your own eval harness for domain-specific agents. The LLM-as-judge patterns that work for multi-step agents..
agent evaluationtrajectory evaluationtask success rateSWE-BenchWebArenaGAIAAgentBencheval harnessLLM-as-judgeturn efficiency
Learning path
Agent Loop Implementation: Control Flow & Edge Cases→Agent Evaluation: Harnesses, Metrics & Benchmarks ← you are here→Agent Reliability, Fallbacks & Human-in-the-Loop Safety
Try a sample question
What is SWE-Bench and what does it measure?
A.A benchmark of real GitHub issues from open-source Python repositories that measures an agent's ability to generate correct code patches that pass the repository's test suite
B.A benchmark measuring how quickly an agent can write new unit tests for a given codebase, scoring agents on test coverage percentage achieved within a fixed time budget
C.A standardized evaluation dataset for measuring LLM code completion accuracy on HumanEval-style single-function synthesis problems without repository context
D.A benchmark that evaluates multi-agent software teams on end-to-end project delivery speed using simulated sprint velocity and ticket throughput metrics
Sign up free to take the full check and see the answer with an explanation.
What this module tests
- Conceptual understanding of agent evaluation: harnesses, metrics & benchmarks fundamentals
- Trade-offs and design decisions in real system scenarios
- agent evaluation and trajectory evaluation — applied knowledge
- Interview-style reasoning and explanation quality
Free to start
Take the Agent Evaluation: Harnesses, Metrics & Benchmarks check →
10 questions · Dimension scoring · AI coach feedback · Shareable result
Test your knowledge — free →No credit card · Takes 10–15 min
Frequently asked questions
- What does the Agent Evaluation: Harnesses, Metrics & Benchmarks check test?
- The check covers evaluating agents is fundamentally harder than evaluating single llm calls. trajectory evaluation vs outcome evaluation. turn-level vs session-level metrics. task success rate, tool call accuracy, over-refusal rate, loop efficiency (turns to completion), cost per successful task. public benchmarks (swe-bench, webarena, gaia, agentbench). building your own eval harness for domain-specific agents. the llm-as-judge patterns that work for multi-step agents.. Questions span beginner to advanced difficulty and test both conceptual understanding and practical trade-off reasoning.
- What keywords should I know for Agent Evaluation: Harnesses, Metrics & Benchmarks?
- Key concepts include: agent evaluation, trajectory evaluation, task success rate, SWE-Bench, WebArena, GAIA, AgentBench, eval harness, LLM-as-judge, turn efficiency. These frequently appear in agentic ai engineering interviews at product and infrastructure companies.
- How many questions are in the check?
- Each check has 10 questions — a mix of MCQ and short-answer. You get scored by dimension (e.g., scalability, consistency) so you know exactly where to focus.
- How long does it take to study Agent Evaluation: Harnesses, Metrics & Benchmarks?
- Most engineers spend around 3 hours on this module. The check itself takes about 10–15 minutes. You can retake it up to 3 times per day.
- What prerequisite knowledge do I need before Agent Evaluation: Harnesses, Metrics & Benchmarks?
- Agent Evaluation: Harnesses, Metrics & Benchmarks builds on Agent Loop Implementation: Control Flow & Edge Cases. Complete those first for the best results.
- Is agent evaluation, trajectory evaluation, task success rate covered?
- Yes. The Agent Evaluation: Harnesses, Metrics & Benchmarks module covers agent evaluation, trajectory evaluation, task success rate among other key concepts. The check includes questions that reflect real agentic ai engineering interview patterns.