Lesson 1276 of 2116
Replaying Agent Runs for Debugging and Regression Testing
Build a replay harness that re-runs a recorded trace against a new prompt or model.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2Building Deterministic Replays for Agent Runs
- 3The premise
- 4AI agents and replay determinism for debugging
Concept cluster
Terms to connect while reading
Section 1
The premise
Without replay, every prompt change is a leap of faith — every fix risks breaking three things that used to work.
What AI does well here
- Re-run a recorded trace deterministically (mocked tool returns)
- Diff the new and old final outputs side by side
- Score regressions across a saved corpus of past runs
- Bisect to the prompt or tool change that caused the regression
What AI cannot do
- Replay non-deterministic tool effects faithfully without stubs
- Detect 'silently fine' regressions without scored evals
- Cover situations the recorded corpus never saw
Key terms in this lesson
Section 2
Building Deterministic Replays for Agent Runs
Section 3
The premise
Record prompts, tool inputs, tool outputs, and seeds; offer a 'replay' command that re-executes the run against the captured trace.
What AI does well here
- Persist a structured trace per run
- Reproduce a failure on demand
- Diff two replays after a model bump
What AI cannot do
- Force non-deterministic models to repeat
- Replay against changed external state
- Capture inputs you didn't instrument
Section 4
AI agents and replay determinism for debugging
Section 5
The premise
Non-replayable agents are nightmare to debug; capturing inputs enables true reproduction.
What AI does well here
- Persist all tool calls and model outputs per run
- Replay against the same model snapshot
What AI cannot do
- Recreate true non-determinism from temperature
- Reproduce side effects in third-party systems
Understanding "AI agents and replay determinism for debugging" in practice: AI agents can take actions, run loops, and call tools — giving one instruction can start a chain of automated steps. Replay an agent run with the same inputs to debug a failure — and knowing how to apply this gives you a concrete advantage.
- Apply replay in your agentic workflow to get better results
- Apply determinism in your agentic workflow to get better results
- Apply debugging in your agentic workflow to get better results
- 1Design an agent spec: goal, tools, permissions, stop condition
- 2Run a simple web-search agent in a sandbox environment
- 3Instrument an existing workflow to identify where an agent could save time
Key terms in this lesson
End-of-lesson quiz
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