Execution Logs
Every workflow execution produces a complete, immutable audit trail — allowing you to debug failures, analyze performance, and understand agent behavior at every step.
Why Execution Logs Matter
In AI automation, failures are inevitable — models hallucinate, APIs timeout, tools misbehave. This platform treats observability as a first-class feature. Logs are not an afterthought; they are the foundation for trust, safety, and reproducibility.
Observability Stack
Run History
Every workflow trigger creates a permanent execution record.
Step-Level Status
Track success, failure, retries, and timing for each step.
Payload Inspection
Full visibility into inputs, outputs, and intermediate results.
Log Structure
Logs are stored per task execution and indexed by workflow, step, and timestamp. This enables efficient querying, debugging, and replay.
| Field | Description | Example |
|---|---|---|
| trace_id | Unique identifier for a full workflow execution | tr_8273ab91 |
| step_id | The workflow step being executed | email_send_01 |
| status | Execution result of the step | SUCCESS | FAILED | RETRY |
| latency | Execution time in milliseconds | 482ms |
| payload | Input parameters and output results | { input, output } |
Where Logs Are Stored
Execution logs are persisted locally in MongoDB under each task record. They are indexed by workflow ID, task ID, step ID, and timestamp.
Failure Debugging Workflow
When a workflow fails, inspect the failed step’s log entry to view the exact error, model output, and tool response. You can replay executions with identical context to validate fixes or tune prompts without re-triggering upstream steps.
Trust & Compliance
Because all logs are stored locally and never leave your environment, this system provides stronger privacy guarantees than cloud-hosted automation platforms. This is critical for sensitive workflows and regulated data.