Does CrewAI have built-in compliance audit logging?

No. CrewAI provides agent orchestration, task management, and tool use — it does not include tamper-evident audit logging, cryptographic signing, or the compliance-structured records required by EU AI Act Article 12 or HIPAA §164.312(b). Adding compliance-grade audit trails to CrewAI requires instrumenting the task execution layer with a decision ledger SDK like Tenet AI.

How do I integrate Tenet AI with CrewAI?

Install pip install tenet-ai-sdk. Initialize TenetClient once at startup. In each agent task execution function, wrap the decision logic with tenet.intent() context manager. Call intent.snapshot_context() to capture task inputs, intent.decide() to record the decision, and intent.execute() to close the record. The Ghost SDK fire-and-forget architecture adds under 0.3ms blocking overhead to task execution.

How do I capture decisions from multiple agents in a CrewAI crew?

Initialize a single TenetClient shared across all agents. Each agent uses tenet.intent() with a distinct agent_id parameter but a common session_id. Tenet automatically correlates decisions by session_id in the compliance report — auditors can trace the full decision chain across agents in a single crew run.

Does adding Tenet AI to CrewAI affect agent performance?

Ghost SDK uses fire-and-forget architecture — tenet.intent() returns in under 0.3ms blocking overhead. All I/O (signing, network write, storage) happens on a background thread. Your CrewAI agents never wait for audit logging to complete. If the Tenet backend is unreachable, the SDK queues events in memory and drains when connectivity is restored.

Can I use Tenet AI with CrewAI tools?

Yes. Capture tool use in the context snapshot by including tool outputs in the intent.snapshot_context() call, or wrap individual tool executions with tenet.trace() for Ghost SDK-style logging. For compliance purposes, capturing tool calls and outputs in the agent-level decision record is usually sufficient.

What CrewAI frameworks does Tenet AI support?

Tenet AI supports all CrewAI versions using Python agent task functions. The integration does not depend on CrewAI internals — it wraps the task execution function at the application level. Works with CrewAI 0.x and 1.x, synchronous and asynchronous task execution, tool use, and any LLM backend (OpenAI, Anthropic, Groq, local models).

Do CrewAI audit records satisfy SOC 2 requirements?

Yes, when captured with Tenet AI. SOC 2 CC7.2 requires anomaly detection across AI decision patterns — Tenet captures decision-level records that satisfy this. SOC 2 CC4.1 requires monitoring of system changes including model version changes — Tenet captures model version in each record's provenance metadata. Compliance PDF export for SOC 2 auditors is available from the Tenet dashboard.

How to Add Compliance Monitoring to CrewAI Agents (EU AI Act & HIPAA)

Q: How do I satisfy EU AI Act Article 14 (human oversight) with CrewAI?

EU AI Act Article 14 requires capturing human overrides with actor, timestamp, reason, and outcome. In CrewAI, implement a human review step that calls tenet.record_override() when a reviewer modifies an agent decision. This creates a linked override record connecting the original AI decision to the human override in the audit trail.

CrewAI provides excellent primitives for multi-agent coordination — agents, tasks, crews, and flows. What it does not provide is a compliance layer: no tamper-evident records, no cryptographic signing, no context snapshots for post-hoc reconstruction, and no compliance PDF export for auditors. This guide shows how to add compliance monitoring to CrewAI agents using the Tenet AI SDK, with working Python code for both single-agent and multi-agent crew patterns.

The Compliance Gap in CrewAI

CrewAI handles orchestration, role assignment, tool use, and task delegation. It does not capture tamper-evident audit records, does not apply cryptographic signing to agent outputs, does not store context snapshots in a replayable format, and does not generate compliance reports for EU AI Act, HIPAA, or SOC 2 auditors. Adding compliance monitoring requires instrumenting the task execution layer — which is precisely where Tenet AI integrates.

Three Integration Patterns

Three patterns for adding compliance to CrewAI: (1) Wrap the task execution function — recommended for most use cases, one Tenet record per agent task, captures intent/context/decision/outcome at the task level. (2) Crew-level wrapper — wrap the entire crew.kickoff() call, appropriate when the crew has a single primary output and individual agent steps are implementation details. (3) Tool-level capture with tenet.trace() — wrap individual tool executions for high-stakes tool use (e.g., credit bureau API calls, patient record lookups) that need individual compliance records.

Single Agent Implementation

Install pip install crewai tenet-ai-sdk. Initialize TenetClient once at startup. In your task execution function, wrap the CrewAI Crew.kickoff() call with tenet.intent(). Call intent.snapshot_context() to capture the application context, create and run the CrewAI task, parse the output, then call intent.decide() with the options and chosen action, and intent.execute() to close the record. The record_id returned by intent.execute() is your tamper-evident reference for auditors.

Multi-Agent Crew: Correlated Records

For multi-agent crews, use a shared session_id across all agent decisions in a single crew run. Each agent uses tenet.intent() with a distinct agent_id but the same session_id. Tenet automatically correlates decisions by session_id in the compliance report — auditors can trace the full decision chain across agents. Example: a claims adjudication crew with document analyst, fraud detector, and final adjudicator agents, each producing a linked record in the same audit session.

Human Oversight (EU AI Act Art. 14)

EU AI Act Article 14 requires high-risk AI systems to enable human oversight and capture when humans override AI decisions. In CrewAI workflows, implement a human review step that calls tenet.record_override() when a reviewer modifies a crew output. This creates a linked override record in the audit trail with actor ID, timestamp, original decision, override decision, and reason — satisfying Article 14 documentation requirements.