Policy Enforcement for CrewAI Agents

Block, route, or approve agent actions in real time in CrewAI agents — without rewriting your code, without adding a proxy to the request path.

The problem

CrewAI is great at what it does, but production-grade policy enforcement is bring-your-own. Most teams discover this when the first production incident hits, the first OpenAI bill arrives, or the first auditor asks for evidence.

Specifically:

Crew-level cost easily runs 5-10× single-agent cost — opaque without tooling
Role-based access control isn't enforced — any agent can call any tool
Long task chains can drift without continuous evaluation

How Prefactor solves it

Prefactor wraps your CrewAI Crew and adds policy enforcement as a runtime layer. Specifically:

require approval for tool calls affecting customer data
block tool calls when PII appears in args
enforce per-agent spend caps
rate-limit specific agents
policy-as-code for compliance teams

Install and integrate

pip install prefactor-crewai

from crewai import Crew
from prefactor import Prefactor

pf = Prefactor(api_key="pf_live_...")
crew = pf.wrap(Crew(agents=[...], tasks=[...]), crew_id="research-crew")

Specific use cases

Require approval for tool calls affecting customer data
Block tool calls when pii appears in args
Enforce per-agent spend caps
Rate-limit specific agents
Policy-as-code for compliance teams

FAQ

Does this add latency? Per-span instrumentation adds ~2-5ms. Telemetry ships asynchronously. No synchronous network call in the agent's hot path unless you enable blocking policy enforcement.

Can I configure this per-agent? Yes. Policy Enforcement settings are per-agent — different agents can have different policies, sampling rates, retention, etc.

Is this compatible with other observability tools? Yes. Prefactor exports OpenTelemetry; works alongside Datadog, Langfuse, LangSmith, and others.

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