Open Data Infrastructure
Agentic Data Replay Logs for Tool Calls
How replay logs should capture inputs, data products, policy decisions, outputs, errors, and corrections for agent incidents.
A tool call log that says only "success" is almost useless after an agent incident. The hard question is what the agent knew when it acted.
Debugging needs replay
OpenTelemetry traces model operations as spans that can capture what happened during a request. OpenLineage models runs, jobs, datasets, and facets. CloudEvents defines a common way to describe event data. Those ideas point toward a practical design for agent tool-call replay logs.
The log should help a team reconstruct the data path. What input arrived? Which tool was selected? Which data products were accessed? Which policy decisions allowed or denied access? What output returned? What correction happened later?
The log has to carry context
Replay does not mean storing every sensitive value forever. It means preserving enough identifiers, hashes, timestamps, policy decisions, source references, and output summaries to investigate without guessing. Sensitive payloads may need redaction, encryption, shorter retention, or pointer-based storage.
The goal is controlled replay. A team should be able to explain the behavior without creating a second data leak in the incident system.
Core idea: Agentic data replay logs should make tool behavior reconstructable without turning incident records into unmanaged data stores.
The ODI replay pattern
Open Data Infrastructure can connect replay logs to catalogs, lineage, policy, and evaluation systems. The replay record should reference data products and policy decisions rather than copying every data value into a generic application log.
For adjacent context, read agentic data product observability, access logs as evaluation evidence, and agentic data quality feedback loops.
What breaks first
- Logs capture prompts and outputs but not source data product identifiers.
- Policy denials are omitted because the tool call did not complete.
- Replay requires raw sensitive data that should not be stored in the trace system.
- Corrections are stored in evaluation tools but never linked back to the original tool call.
Questions to ask
Ask which fields are required for replay, which values are redacted, and how long each record is retained. Ask whether traces, lineage, catalog metadata, and evaluation results share stable identifiers.
If the tool can act, the platform must be able to replay why.
Sources to start with
These primary sources anchor the technical claims in this guide.
- OpenTelemetry traces documentation
- OpenLineage object model documentation
- OpenLineage facets documentation
- CloudEvents specification overview
Replay is the difference between observability and regret.