Score, explain, and record whether an AI agent should trust a tool, response, peer, memory, or destination before acting.
Every agent action gets a verdict, confidence level, recommended action, and audit-ready proof record.
Enter a URL to see how ActionLedger scores an agent action before execution. Verdict confidence tells you how sure the system is — not how safe the URL is.
Use examples to see how ActionLedger handles safe destinations, impersonation, redirects, malicious responses, and exploit-pattern payloads.
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View Quickstart →AI agents are no longer just generating text. They call tools, fetch URLs, process files, update memory, delegate work, and interact with other agents. Every one of those actions can introduce risk.
ActionLedger evaluates those actions before they happen, explains the decision, and records the evidence so teams know what was allowed, blocked, or escalated — and why.
Score. Explain. Record. Govern.
Evaluate the requested tool, URL, response, peer, memory, or destination.
Return a risk score, verdict confidence, risk factors, and recommended action.
Apply policy to allow, block, or require human review.
Create an audit-ready record showing what was checked, why it mattered, and what decision was made.
Four capabilities that make agent actions trustworthy, auditable, and governable.
Fast action scoring before agent actions execute. Every interaction gets a verdict, confidence level, and recommended action in under 50ms.
Evidence and receipts showing why an action was allowed, blocked, or reviewed. Signed and tamper-evident for audit and compliance.
Canonical goals, decisions, definitions, tasks, failed attempts, and handoffs readable by every agent in the workflow.
Peer-agent trust checks, semantic drift detection, and provenance for delegated work. Human review for uncertain or high-risk handoffs.
Use ActionLedger through the live scanner, API, or SDK.