Setting up tracing and auditing of AI agent actions in Paperclip
Autonomous AI agents make decisions and perform actions. For enterprise use, full traceability is critical: who set the task, what the agent decided, what tools it invoked, what response it received, and what decision it made.
What is being traced?
Full trace of task execution:
- Incoming task (who, when, content)
- Each LLM call (prompt, response, tokens, model, cost, time)
- Each tool call (name, parameters, result, time)
- Agent decisions (path choice, reason for escalation)
- The final result
Storage: PostgreSQL for structured data. S3/MinIO for full prompts and responses (large data). Retention policy: default 90 days, configurable.
Integration with observability platforms
LangSmith (LangChain): specialized tracing for LLM. Call chain visualization, trace search, evaluation.
Weights & Biases (W&B): if you need to monitor the quality of responses over time.
Datadog / Grafana: system metrics + custom metrics for agent tasks.
Audit Log
An immutable log of all actions (append-only). Each entry is signed to detect tampering. Used for compliance audits, incident investigations, and agent action verification.