01
Listen
Add the Actrail SDK, MCP middleware, or tool wrapper where your agents already run.
SDKMCPWrapper
How it works→See what your AI agents are doing
See what your AI agents are doing
before you trust them to act.
Helping teams catch risky agent actions in real time and turn every Slack review into runtime policy.
support-refund-agent
read · ticket
fetch · profile
read · PII
refund · $7,200
send · email
ActrailAPPRisky action trail
Support Agent issued a $7,200 refund with no approval step.
How it works
One hook. Every risky trail reviewed.
No business impact. No production blocking. Ship agents with confidence.
02
Rebuild
Actrail stitches tool calls into one action trail, so risk is reviewed across the full path.
readfetchrefundsend
03
Review
Risky trails land in Slack. Your team reviews them, and Actrail updates policy for future actions.
ReviewCreate policyDismiss
Risk detectors
The risk is not one call. It is the sequence.
Sensitive data shared externally
Customer data read, then posted to an outside channel or email.
read · customer→compose→send · external
Important action without approval
Refunds, access changes, deletes, or deploys without human review.
trigger→refund · execute
Untrusted input causes action
A customer message or webpage drives a business-critical action.
inbound msg→act
Tool output manipulates the agent
A document or site tells the agent to ignore rules, and the agent follows it.
fetch · doc→override · rules
New risky tool usage
An agent starts using a tool that can write, export, delete, or modify data.
first use→delete · export
Slack-first review
Review risky trails where your team already works.
- Not another telemetry dashboard to check
- Only the risky trails reach Slack — with the agent, path, and risk explanation
- True positive, false positive, expected behavior
- Create policy from an observed trail
ActrailAPP2:14 PM
Risky action trail
- Agent
- Support Refund Agent
- Trail
- Ticket → Profile → Refund API → Email
- Why
- High-value refund after PII access with no approval step.
- Mode
- Shadow only
Recorded — Actrail will fold this into runtime policy.
The compounding loop
Every review makes enforcement safer later.
01Readaction trails
02Reviewrisky behavior
03Hardenruntime policy
04Recommendapprovals
05Enforcewith confidence
14-day pilot
Run Actrail alongside your agents.
We will show the risky AI action trails your agents are already taking, then turn reviewed feedback into starter runtime policies.
Pilot output
- Risky trails detected
- True and false positive labels
- Top risky agents and tools
- Initial runtime policies
- Approval and enforcement candidates
FAQ
Built for teams already shipping agents into production.
Who is Actrail for?
AI platform, security engineering, and AppSec teams responsible for production agents that can read, write, export, refund, deploy, or message.
Why not block immediately?
A false positive can break a live workflow. Actrail starts in shadow mode so teams learn what should be reviewed before they enforce.
How is this different from generic monitoring?
Actrail focuses on action trails and review-driven runtime policy, not raw traces, prompt filtering, or gateway routing.
Start by watching