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How to Know Your AI Agent Actually Works
Created by Shaunak Ghosh
Build a repeatable workflow to make AI agents reliable enough to ship. You will define measurable reliability targets, set up a dataset-driven eval loop you can run locally and in CI, validate that prompt changes truly fix issues, and apply ship-fast patterns like tool contracts and latency budgets.
Requirements
- Experience shipping LLM features with tool/API calls
- Comfort with JSON/schema validation and API error handling patterns
- Working knowledge of regression testing and CI gating concepts
- Ability to read basic latency percentiles (P50/P95) and reason about timeouts
What you'll learn
- Define agent reliability in terms of testable failure modes and input-layer contracts, not “prompt quality.”
- Build a repeatable eval harness with a fixed dataset, rubric-driven scoring, and a judge you have calibrated against human expectations.
- Use ablations to determine whether a prompt or constraint change fixed a root cause versus producing a measurement artifact.
Learning path
4 modules • Each builds on the previous one
1
Agent reliability targets and failure modes
Define what “reliable” means for an agent by turning vague quality goals into measurable success criteria, constraint checks, and an error budget. Map typical agent failures into categories that drive what you test and instrument.
1 video6 min
2
Repeatable eval harness for prompts
Build a clean, repeatable evaluation loop using a versioned dataset of representative cases, automated graders, and regression gates. Learn how modern eval tooling fits into CI so prompt and model changes don’t silently ship regressions.
1 video13 min
3
Masking vs fixing: robust validation
Learn a repeatable method to detect whether a prompt change actually fixes root-cause errors or merely overfits your test set and hides failures. Use ablations, holdouts, metamorphic checks, and adversarial inputs to stress claims of improvement.
1 video6 min
4
Fast shipping tactics for resilient agents
Apply the tactics experienced agentic developers use to move fast without fragility: strong tool contracts, explicit state/workflow control, durability, and observability-driven iteration. Focus on patterns that reduce nondeterminism, make failures debuggable, and enable safe releases.
1 video7 min
Start Learning
Begin your learning journey
Modules4
Duration30 min
Science-backed learning
In-video quizzes and scaffolded content to maximize retention.
Key concepts
Failure Taxonomies And Measurable Reliability TargetsInput Validation Contracts And Confidence-Based RoutingVersioned Eval Datasets With Rubric-Based Scoring