AI agent replay
Opswald turns production failures into replayable traces. Pin prompts, retrieved context, tool outputs, model decisions, and state transitions so teams can reproduce incidents and test fixes.
Direct answer
AI agent replay is the controlled reproduction of a prior agent run with the original prompt, retrieved context, memory, tool outputs, decisions, and side-effect receipts preserved. Instead of simply rerunning a non-deterministic workflow, replay pins the evidence that caused the incident and varies one change at a time so engineers can prove a fix.
What breaks
They happen across prompts, memory, retrieved documents, tool schemas, model choices, retries, and side effects. Opswald is built to make that chain inspectable instead of asking engineers to reconstruct it from logs.
Temperature, retrieval, cache state, and tool availability change between production and local debugging.
Unit tests usually preserve inputs and outputs, but not the intermediate evidence the agent used to choose actions.
A prompt patch can fix the visible bug while changing other decisions in the same workflow.
Incident traces rarely become durable tests because reconstructing the run is too manual.
A good replay system does not simply run the agent again. It controls what changed so engineers can isolate the first meaningful divergence.
pin: prompt, context, memory, tool_outputs stub: charge_card, send_email, write_ticket compare: decision_graph, tool_args, final_state expect: no duplicate mutation and policy evidence cited promote: incident trace becomes regression fixture
Practical debugging
Recreate the exact context behind a production failure without asking the customer to trigger it again.
See whether a prompt or tool-description change alters important decision paths.
Replay old traces against stricter schemas and safer retry behavior.
Replay walkthrough
A realistic replay starts with a failed production run, not a clean-room test. This walkthrough shows the evidence Opswald would preserve before engineers vary the prompt, tool schema, or retry policy.
Captured evidence: Customer request, trace ID, agent version, model, system prompt, retrieved policy snippets, approval token, and the original refund tool arguments.
Replay check: The replay starts from the same prompt/context bundle and does not fetch fresh policy text or memory.
Captured evidence: First `refund_customer` response, payment-provider receipt ID, timeout envelope, retry count, and final customer-support ticket state.
Replay check: The tool is stubbed with captured receipts so the agent sees the same timeout without issuing a second refund.
Captured evidence: Planner step, approval gate, retry branch, duplicate-mutation guard result, and the model message that justified retrying.
Replay check: Opswald highlights the first divergent branch when a patched prompt or schema chooses `stop_and_escalate` instead of retrying.
Captured evidence: Normalized timestamps, redacted customer fields, expected tool-call sequence, and final state assertion.
Replay check: Future prompt, model, retrieval, and tool-schema changes must pass the fixture before shipping.
Comparison
Keep reading
FAQ
No. Agent replay is controlled comparison. You pin the original evidence and deliberately vary one part—prompt, model, tool schema, retrieval, or orchestration—to see what changes.
Use stubs, dry-run modes, sandbox accounts, and captured receipts so the agent sees realistic state without mutating production again.
Capture the original user request, system prompt, retrieved documents, memory reads, model responses, tool schemas, tool arguments, tool outputs, retries, errors, permissions, and external side-effect receipts.
Replay lets teams run the same incident trace against a proposed prompt and compare decision paths, tool calls, and final state before shipping the change.
Yes. A failed production trace can become a pinned fixture that runs against future prompt, model, retrieval, schema, and orchestration changes.
Opswald is in early access for teams shipping AI agents that call tools, use MCP servers, or run multi-step workflows in production.
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