AI agents are calling APIs, querying databases, and making decisions in production. If something goes wrong, can you prove what happened?

The @audit decorator from asqav wraps any Python function with a tamper-evident audit trail. No infrastructure changes, no database to manage.

Before

def process_claim(claim_id):
    analysis = llm.invoke(f"Analyze claim {claim_id}")
    decision = llm.invoke(f"Approve or reject: {analysis}")
    return decision

No record of what the LLM returned. No proof a human reviewed it. No way to reproduce the decision.

After

from asqav import audit

@audit
def process_claim(claim_id):
    analysis = llm.invoke(f"Analyze claim {claim_id}")
    decision = llm.invoke(f"Approve or reject: {analysis}")
    return decision

Now every call is logged with:

  • Full input and output
  • Cryptographic signature (quantum-safe ML-DSA)
  • Timestamp and execution context
  • Policy evaluation results

The audit trail is tamper-evident. If anyone modifies a log entry, the signature breaks.

Policy enforcement

You can also block or flag actions in real-time:

from asqav import audit, policy

@policy(max_tokens=1000, require_approval=True)
@audit
def high_risk_decision(data):
    return agent.run(data)

Install

pip install asqav

MIT licensed. Source on GitHub.