Agent Evaluation, Red-Teaming & Reliability Engineering
Prove your agents are safe, robust, and reliable — under the kind of scrutiny that ends careers when it's skipped.
About this course
This course is for engineers and teams whose agents are already live and now have to survive the harder questions: not 'does it work in the demo' but 'can you prove, with evidence, that it won't do something dangerous, expensive, or embarrassing at 3am when nobody's watching.' You'll build a full evaluation discipline from the ground up — statistically sound eval design that doesn't collapse under small sample sizes, an automated eval harness with calibrated LLM-as-judge scoring that resists gaming, and a structured adversarial red-teaming process modeled on how real security teams attack real systems, adapted for the specific ways agents fail (tool misuse, goal hijacking, data exfiltration through tool calls, jailbreaks buried in conversation history). From there the course moves into reliability engineering proper: chaos-engineering practices adapted for non-deterministic agent systems, SLOs and error budgets for behavior instead of just uptime, human-in-the-loop escalation systems designed to survive real volume instead of collapsing at the first spike, and a full incident-response and blameless-postmortem framework for when an agent does something wrong in production — because it will. Every module pairs a rigorous framework with worked numbers and concrete artifacts (eval harness code, attack taxonomies, SLO worksheets, postmortem templates) you can adapt directly. This is the material that separates a team that can ship an agent demo from a team that can put an agent in front of paying customers, regulators, and an incident review board and defend every decision. It is dense, opinionated, and built for engineers who are done hoping their agent behaves and are ready to prove it.
What you'll learn
- ✓Design statistically sound evaluation methodology for agent systems — golden datasets with real coverage, trajectory-level and task-level metrics, and confidence intervals that are honest about small-sample uncertainty
- ✓Build and operate an automated eval harness with a calibrated, gaming-resistant LLM-as-judge pipeline wired into deployment as a real gate, not a dashboard nobody reads
- ✓Plan and execute a structured adversarial red-team exercise against an agent system using an attack taxonomy covering tool misuse, goal hijacking, data exfiltration via tool calls, and conversation-history jailbreaks
- ✓Apply chaos-engineering practices to agent infrastructure — fault injection, malformed observations, partial state corruption, and game days — to surface failure modes before customers do
- ✓Define SLIs, SLOs, and error budgets for non-deterministic agent behavior, and build alerting that catches behavioral drift instead of only uptime and latency
- ✓Design a human-in-the-loop escalation system with triage, confidence-based routing, and reviewer tooling that scales past a few dozen users without collapsing the review queue
- ✓Run a real incident response process for an agent failure and write a blameless postmortem that measurably feeds back into your eval suite, red-team corpus, and guardrails
Curriculum
Module 1
Foundations of Agent Evaluation
What evaluation actually means once you move from classic ML metrics to non-deterministic, multi-step agent behavior — golden dataset design, and the distinction between judging a destination and judging a path.
- What 'Evaluation' Means for Agents (and Why Classic ML Eval Doesn't Transfer)45 min
- Golden Datasets: Coverage, Curation, and Keeping Them Alive46 min
- Task-Level vs. Trajectory-Level Evaluation: Judging the Path, Not Just the Destination44 min
- Module 1 Quiz: Foundations of Agent EvaluationQuiz
Module 2
Building Automated Eval Pipelines
Real eval harness architecture, LLM-as-judge done with genuine rigor and resistance to gaming, and the statistical discipline required to trust a result computed from a small number of trials.
- Eval Harness Architecture: Runners, Graders, and Score Storage48 min
- LLM-as-Judge Done Rigorously: Calibration and Avoiding Eval Gaming48 min
- Statistical Significance for Small Sample Sizes46 min
- Module 2 Quiz: Building Automated Eval PipelinesQuiz
Module 3
Adversarial Red-Teaming Methodology
A structured, repeatable process for attacking your own agent before an attacker does — attack taxonomies specific to agentic systems, how to actually run an exercise, and turning findings into locked-in regressions.
- Attack Taxonomies for Agentic Systems47 min
- Running a Structured Red-Team Exercise47 min
- From Findings to Fixes: Triage, Severity, and Locking In Regressions45 min
- Module 3 Quiz: Adversarial Red-Teaming MethodologyQuiz
Module 4
Chaos Engineering for Agent Systems
Deliberately injecting tool failures, latency, malformed observations, and partial state corruption into agent systems to find failure modes before users do, and designing structured game days for agent infrastructure.
- Principles of Chaos Engineering, Adapted for Non-Deterministic Agents44 min
- Fault Injection Techniques: Tool Failures, Latency, Malformed Observations, Partial State Corruption46 min
- Designing Game Days for Agent Infrastructure45 min
- Module 4 Quiz: Chaos Engineering for Agent SystemsQuiz
Module 5
Reliability Targets: SLOs, Error Budgets & Alerting for Agent Behavior
Defining what 'reliable' means for a system whose correct behavior isn't a single fixed answer, setting realistic SLOs and error budgets for agent behavior, and alerting on behavioral drift rather than only uptime.
- What 'Reliable' Even Means for a Non-Deterministic System44 min
- Defining SLIs, SLOs, and Error Budgets for Agent Behavior46 min
- Alerting on Behavioral Drift, Not Just Uptime45 min
- Module 5 Quiz: Reliability Targets, SLOs, Error Budgets & AlertingQuiz
Module 6
Human-in-the-Loop at Scale
Designing escalation systems that don't collapse under real volume — triage, confidence-based routing, reviewer tooling, and the feedback loop that turns human review into permanent system improvement.
- Escalation Design Fundamentals: Triage and Confidence-Based Routing45 min
- Reviewer Tooling and Queue Management at Volume45 min
- Feedback Loops: Turning Human Review into Eval and Guardrail Improvements44 min
- Module 6 Quiz: Human-in-the-Loop at ScaleQuiz
Module 7
Incident Response & Postmortems for Agent Failures
A real incident-response framework adapted for the specific ways agents fail, writing blameless postmortems that actually change the system, and closing every module's feedback loop back into evals, red-teaming, and guardrails.
- An Incident-Response Framework for Agent-Specific Failures46 min
- Writing Blameless Postmortems That Actually Change the System46 min
- Closing the Loop: Postmortem Findings into Evals, Red-Team Suites, and Guardrails43 min
- Module 7 Quiz: Incident Response & Postmortems for Agent FailuresQuiz
Agent Evaluation, Red-Teaming & Reliability Engineering — Final Exam
Comprehensive final exam covering the entire course.