Governed Action Records
Governed records for AI-mediated work.
ActBound turns fragmented agent, tool, policy, approval, identity, and runtime evidence into governed records reviewers can trust.
Evidence fragments
Identity
captured
Policy
captured
Approval
captured
Tool Call
captured
Runtime
captured
Evidence
captured
Governed Action Record
GAR-0481
Record status
Coherent
Review flag
None
Paired signals
6 / 6
Expected evidence profile
Satisfied
The problem
AI work is becoming harder to reconstruct.
As agents, tools, models, approvals, and human decisions spread across systems, the record of what happened becomes fragmented. Logs exist. Policies exist. Approvals exist. Tool traces exist. But the governed account of the action often has to be rebuilt manually after the fact.
Fragmented evidence
Signals live in logs, policy engines, approval tools, and runtime traces that were never designed to be read together.
Missing context
Raw traces rarely show who was authorized, what was expected, and which decision the action belonged to.
Manual review burden
Reconstructing what happened falls to engineers, security teams, and reviewers after the action is already complete.
Product
GAR: the Governed Action Record
GAR creates a structured record around high-consequence AI-mediated actions. It connects identity, policy, approval, runtime, tool, and evidence signals into a reviewable package that helps teams understand what happened, where evidence matches expectations, and what requires review.
01
Identity
Who or what acted
02
Policy
What was allowed
03
Approval
What was authorized
04
Runtime
What executed
05
Tool Evidence
What it touched
06
Review Record
What reviewers see
How it works
From scattered signals to governed review.
01
Ingest evidence from existing systems
Connect logs, policy engines, approval tools, and runtime traces you already run.
02
Normalize actions into canonical records
Resolve scattered signals into a single canonical account of the action.
03
Compare against expected evidence profiles
Check each record against the evidence that should be present.
04
Export reviewable packages
Give reviewers a coherent package for human review, audit, or escalation.
Use cases
Built for sensitive AI workflows.
Agentic tool use
Record what autonomous agents requested, were allowed to do, and actually executed.
Security review
Give reviewers a coherent account of sensitive actions instead of scattered logs.
Policy and approval reconciliation
See where policy, approvals, and execution agree — and where they diverge.
Regulated workflow review
Assemble defensible records for workflows that face external scrutiny.
Incident reconstruction
Rebuild what happened across systems when an action needs to be explained.
Enterprise AI governance
Standardize how high-consequence AI actions are recorded, reconciled, and reviewed.
Trust boundary
A record layer, not a magic oracle.
ActBound does not replace runtime controls, SIEMs, GRC systems, policy engines, or human judgment. It helps teams assemble and reconcile the governed record of an action so reviewers can see where evidence is coherent, incomplete, conflicted, or missing.
Coherent
Evidence lines up across signals.
Partial
Some expected evidence is missing.
Conflicted
Signals disagree about what happened.
Unpaired
An action has no matching record.
Audience
For teams responsible for accountable AI systems.
AI platform teams
Own how agents and tools act in production.
Security teams
Review sensitive actions and investigate incidents.
Risk and compliance teams
Need defensible records of what occurred.
Enterprise architecture teams
Standardize controls across AI systems.
Legal and audit stakeholders
Require a clear account after the fact.
Design partners
ActBound is seeking design partners with real AI-agent, sensitive tool-use, security review, or regulated workflow environments. The goal is to pressure-test GAR against actual evidence patterns, review needs, and institutional constraints.