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Phase 3·Technology & Computer Science·Intermediate
AI Agent Evaluation for Software Engineers
Evaluate AI agents rigorously as a software engineer — traces, tool use, rubrics, and evaluation pipelines. Built for technical contributors moving into agent evaluation work.
The Roadmap
1
Start Here
What AI agent evaluation is and how engineer-led review is structured.
Introduction to AI Agent Evaluation
Why evaluation is the bottleneck
- What Is an AI Agent?
- Why Evaluation Matters for Product Quality & Safety
- How Engineer Evaluation Work Is Structured Remotely
- Setting Expectations: Quality, Speed & Judgment
Evaluation Fundamentals
Rubrics, metrics, and gold standards
- Rubrics, Scorecards & Pass/Fail Criteria
- Human Preference vs. Automated Metrics
- Gold Sets, Seed Tasks & Calibration
- Inter-Rater Agreement & Consistency
- Documenting Decisions So Others Can Reproduce Them
2
Engineer Evaluation Skills
Build and run evaluation systems around agents, tools, and workflows.
Evaluating Agent Behavior & Tool Use
Traces, actions, and failure modes
- Reading Agent Traces & Tool Calls
- Correctness, Completeness & Safety Checks
- Detecting Hallucinations & Overconfidence
- Tool Misuse, Looping & Incomplete Plans
- Writing Reproducible Bug Reports for Agents
Building Evaluation Pipelines
From ad-hoc review to a repeatable system
- Task Design for Agent Benchmarks
- Logging, Sampling & Review Queues
- Automated Checks + Human Review Layers
- Regression Sets When Models or Prompts Change
- Shipping Evaluation Feedback Into Product Loops
JSON CSV Git
3
Apply & Contribute
Turn evaluation skill into portfolio evidence and paid contributor work.
Ethics, Safety & Responsible Evaluation
Critical judgment under real stakes
- What Never Belongs in Evaluation Data
- Safety, Harm & Policy Violations
- Honest Disagreement Without Blocking Progress
- Responsible Disclosure of Model Failures
- Where Regulation & Standards Are Headed
Your Evaluation Portfolio
From practice tasks to contributor readiness
- Building a Sample Evaluation Case Study
- Finding Remote AI Evaluation Opportunities
- Your 30-Day Contributor Practice Plan
- Capstone: Full Agent Evaluation ReportCapstone
What You'll Achieve
- Evaluate AI agent outputs with clear rubrics and reproducible criteria
- Read agent traces and spot tool misuse, looping, and unsafe actions
- Design evaluation tasks and regression sets for agent systems
- Ship evaluation feedback into product and model improvement loops
- Package evaluation skills for remote engineer contributor roles
Ready to begin?
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