Define policy
Set enforceable rules for what agents can do and under which conditions.
AGENT AUTHORIZATIONSee every action your AI agent takes.
See every action your AI agent takes.
Before it takes it.
Add three lines to any agent. Every outbound action - emails, Slack messages, CRM updates - appears in a live feed. Policies decide automatically. Humans review anything uncertain.
Your agent just tried to email a competitor. DataCrawl blocked it. You did not have to do anything.
No infrastructure to runWorks with any agent frameworkAudit trail from day one
Install in 5 minutes
Three lines. Any agent. Every action governed.
pip install datacrawl
from datacrawl import DataCrawl
dc = DataCrawl(api_key="...")
dc.protect("gmail") # applies safe-mode policy
# Now wrap any tool call:
result = dc.authorize("gmail.send_email", payload)
if result.decision == "allow":
send_email(payload)
dc.record(result.request_id, "success")Works with LangChain, LangGraph, AutoGPT, CrewAI, or any custom agent.
See the demo
Watch the Loom walkthrough.
Prefer full-screen? Open /demo.
How governance works
Governance requires enforcement before execution
Prompt guardrails are guidance. DataCrawl is runtime enforcement. Every action is checked against policy and permissions before anything executes, with a decision and trace for every outcome.
Set permissions
Authorize tools and actions per agent, not just per prompt.
Enforce decisions
Every proposed action is allowed, held for approval, or blocked before execution.
Audit everything
Store payload, policy snapshot, decision, and reviewer trace for complete evidence.
Enforcement modes let you start in monitor mode and move to hard enforcement as confidence grows.
What prompt guardrails miss
Observability tells you what happened after the fact. Prompt guardrails suggest behavior. DataCrawl enforces controls before execution.
| Approach | Scope | Agent registry | Policy engine | Approval flow | Audit trail | Framework-agnostic | Pricing | Reality |
|---|---|---|---|---|---|---|---|---|
| No governance (status quo) | Your risk | ✘ | ✘ | ✘ | ✘ | ✔ | Free until something breaks | Agents act. You find out from a customer. Then you dig through logs and guess what happened. |
| Agent-side guardrails | Per-agent | ✘ | ~ | ✘ | ✘ | ✘ | Engineering time | Works only on agents you built. No shared policy. No central audit. Five agents means five different guardrail systems. |
| LLM observability (LangSmith, Langfuse) | Trace only | ✘ | ✘ | ✘ | ~ | ~ | $50-$500/mo | Tells you what happened after. Cannot stop an action before it executes. No approval chain. |
| Custom approval workflows | Internal | ✘ | ~ | ~ | ~ | ✘ | Months of dev time | Brittle. Tied to one framework. No policy versioning. Breaks when agents change. Becomes its own maintenance burden. |
| DataCrawl | Early | ✔ | ✔ | ✔ | ✔ | ✔ | Talk to us | Purpose-built validation infrastructure. Sits before execution. Works with any agent framework. Every decision is traced and versioned. |
The regulatory case for enforceable governance
EU AI Act
Human oversight of high-risk AI decisions
Article 9 requires human oversight mechanisms. The approval workflow in DataCrawl is that mechanism - an interruption point before execution where a human makes the call.
SOC 2 Type II
Audit trail retention and access controls
Every governance decision is logged with the exact policy version that evaluated it. One export provides the full evidence trail an auditor needs.
ISO 42001
AI management system requirements
Risk tiering, policy versioning and agent capability controls map directly to ISO 42001 AI governance management requirements.
Frequently asked questions
Questions about enforceability, permissions, and audit evidence in real agent deployments.
Would love to connect
Stop relying on prompts as your governance layer.
DataCrawl gives you enforceable policy, tool permissions, and complete execution auditing for production AI agents.
Free tier available · No credit card required · Works with any agent framework
