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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.
4 modules • Each builds on the previous one
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.
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.
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.
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.
Begin your learning journey
In-video quizzes and scaffolded content to maximize retention.