Agentic AI governance
Your agents are already acting. Your governance program isn't ready.
Traditional AI governance assumes a human reviews before anything happens. AI agents break that assumption: they call APIs, execute transactions, send communications, and interact with other systems autonomously — often before any governance team knows they exist. Trustible gives you the structure to register, assess, and maintain documented oversight of every agent in your portfolio, with the audit trail regulators and boards will eventually require.
By the numbers
9
questions to assess any agent's risk profile at intake
215+
rules in the risk engine covering agentic-specific attributes
100%
of agent use cases under the same audit trail as the rest of your portfolio
The problem
Governance built for outputs can't govern autonomous action
Agents act before a human reviews anything. If your program still assumes a checkpoint that no longer exists, these gaps are already open.
You have no structured inventory of what your agents can actually do
Business units deploy agents without telling you — and no one tracks what they're authorized to do, what data they access, or who owns them when something breaks.
Your intake process was built for systems where a human reviews the output
Agents don't work that way, and your governance forms don't ask the right questions.
You can't answer a basic accountability question
If an agent takes an irreversible action — executes a transaction, deletes a record, sends a customer communication — who is responsible, and what does the evidence trail look like?
Your assessments don't distinguish a read-only agent from a tool-chaining one
An agent that calls one read-only API and one that chains tools, runs code, and takes persistent actions get the same form. Neither is assessed correctly.
You have no process for catching agents that drift from their approved scope
Expanded tool access, new tasks, new data sources — none of it triggers a fresh review.
You can't say which agents fall under the EU AI Act's high-risk provisions
When a regulator, auditor, or your own board asks, you don't have a defensible answer.
How it works
Here's how Trustible governs agentic AI.
Four capabilities establish the authorization boundary before an agent deploys — and keep it documented, assessed, and accountable for as long as the agent runs.
Capability 1
Agent intake and registration
Trustible captures agentic use cases directly in the AI Inventory through structured intake with dedicated agentic questions: how the agent is triggered, what tools and data it can access, whether its actions are reversible, how long it runs without a human checkpoint, and whether it interacts with other agents.
- Dedicated agentic intake questions, public-link or in-app
- Captures trigger, tool access, data scope, reversibility, runtime
- Every agent gets a governance record from day one
Why this matters: Agentic use cases are registered alongside all AI in your portfolio — no separate tracking, no parallel systems, one source of truth.
Agent intake · Customer refund agent9 questions
Trigger typeEvent · webhook
Tool / API access
Data accessCustomer PII
Actions reversible?No
Interacts with other agents?Yes
9 agentic questions
Purpose-built for autonomy, access, and reversibility.
One inventory record
Agents tracked next to every other AI asset.
Capability 2
Action-level risk assessment
Trustible's rules engine evaluates agentic risk across the attributes that matter for autonomous systems: autonomy level, external access scope, data sensitivity, irreversibility of actions, agent-to-agent interaction, and prompt-injection exposure. Dedicated scenarios and scoring rules surface the gaps generic intake misses.
- Scores autonomy, external access, data sensitivity, irreversibility
- Agent-to-agent interaction and prompt-injection exposure weighted
- Dedicated agentic risk scenarios, mitigations, and scoring rules
Why this matters: Over 215 boolean rules are evaluated per submission, with dedicated agentic attributes covering data access scope, trigger type, autonomy level, and irreversibility.
Agentic risk profile215 rules evaluated
Autonomy levelHigh
External access scopeBroad
Irreversibility of actionsHigh
Prompt-injection exposureExposed
Assessed tierHigh risk
215+ rules
Boolean logic evaluated on every agent submission.
6 agentic attributes
Risk dimensions unique to autonomous systems.
Capability 3
Oversight reviews and change tracking
When an agent's scope changes — new tool access, expanded data permissions, modified trigger conditions, a new model version — Trustible's substantial modification workflow requires re-governance before the change is treated as approved. Scheduled periodic reviews ensure agents aren't running indefinitely under stale assessments.
- Substantial-modification workflow gates changes before approval
- Triggers on new tools, data, trigger conditions, or model version
- Scheduled periodic reviews keep assessments current
Why this matters: Re-governance is triggered automatically on material changes, with audit-trail documentation of every scope change and review decision.
Change workflow · Refund agent
Scope change: +tool send_email
Re-governance triggered
Risk re-assessmentIn progress
Re-approval
Auto re-governance
Material changes can't sail through unreviewed.
Scheduled reviews
Cadence calibrated to each agent's risk tier.
Capability 4
Accountability and audit trail
Every governance action tied to an agent — intake decisions, risk scores and the rules that generated them, human overrides with documented rationale, approvals, periodic review outcomes — is logged with field-level precision and linked permanently to the agent's record. When an incident occurs, the documentation already exists.
- Field-level log of scores, the rules behind them, and overrides
- Permanently linked to each agent's governance record
- Time-travel query, exportable in ECS format for SIEM
Why this matters: Full time-travel audit history — query any agent's governance record at any point in its lifecycle, exportable in ECS format for SIEM integration.
Audit trail · Refund agent
Registered by M. Reyes
Risk scored: High (rule set v4)
Override: autonomy Med → High
Approved with conditions
Exported to SIEM (ECS)1-click
Time-travel history
Reconstruct any agent's governance state, any date.
ECS export
Feed governance records straight into your SIEM.
See how Trustible registers, assesses, and tracks an autonomous agent end-to-end in a live walkthrough tailored to your stack.
Category definition
What is agentic AI governance?
Defining the discipline
Agentic AI governance is the practice of establishing structured oversight for AI systems that take autonomous action — calling APIs, executing transactions, sending communications, or interacting with other AI agents without requiring human approval at each step.
Unlike traditional AI governance, which focuses on reviewing outputs before humans act on them, agentic AI governance must address the authorization, scope, accountability, and auditability of AI behavior that happens independently of human intervention. The core questions are distinct: What is the agent authorized to do? What can it access? What happens when it acts outside expected parameters, or causes harm through an action no human specifically approved?
As AI agents proliferate across enterprises through tools like Model Context Protocol (MCP), answering these questions with documented, auditable governance processes has become a regulatory and operational requirement — not an aspiration.
90-day rollout
From shadow agents to structured oversight in 90 days
A staged path from discovering what's already running to a fully governed, executive-visible agent portfolio.
Days 1–30
Establish your agent inventory
Known agents registered100%
Shadow agents flagged6
Owners assignedAll
Register every known agentic use case with trigger type, tool access, data scope, autonomy, and ownership. Surface shadow agents and prioritize by access and reversibility.
100% of known agents registered & owned
Days 31–60
Assess risk & apply proportional governance
Agents risk-assessed100%
High-risk → impact reviewRouted
Accountability chainsDocumented
Score every agent across autonomy, data sensitivity, access, and irreversibility. Route higher-risk agents to deeper assessment; fast-track low-risk with documented approval.
Every agent has a documented risk tier
Days 61–90
Operationalize ongoing oversight
Periodic reviewsActive
Modification workflowsOn
Portfolio dashboardLive
Activate periodic reviews and substantial-modification workflows. Deliver an executive view of agents by risk tier, review status, ownership, and open governance gaps.
Executive-ready agentic portfolio visibility
Common questions
What buyers ask about agentic governance
Does Trustible govern agent actions in real time, or only at intake?
Trustible is the governance layer, not the runtime enforcement layer — it doesn't intercept actions mid-execution or block tool calls. It establishes the authorization boundary before deployment (what the agent is approved to do, at what autonomy, with what access) and maintains the oversight that keeps those boundaries re-examined. Runtime enforcement lives at the infrastructure layer — MCP allowlists, identity controls, endpoint monitoring — calibrated against the governance record Trustible provides.
How does Trustible handle agents deployed by business units without governance involvement?
Shadow agents are one of the biggest governance gaps. Trustible's intake surfaces agentic-specific questions whenever any AI use case is submitted, so autonomous systems aren't mistaken for standard AI tools. Agents already deployed without a record can be registered and retroactively governed in the AI Inventory. Identifying shadow agents fully also requires infrastructure-level detection — network monitoring of MCP traffic, MDM, and runtime environment monitoring.
How do you assess risk for agents that interact with other agents?
Agent-to-agent interaction is captured explicitly in agentic intake. When an agent can call or be called by others, the risk engine adds weight to data access, irreversibility, and accountability — because errors and hallucinations propagate through agent chains in ways that compound risk. The record documents which agents interact, what each can access, and who is accountable for the chain's outputs, creating the evidence trail an incident investigation needs.
Does the EU AI Act apply to AI agents, and how does Trustible help?
Yes. Agents that make or significantly influence decisions in high-risk contexts — employment, credit, education, law enforcement, critical infrastructure — are subject to the Act's requirements for risk management, technical documentation, human oversight, and post-market monitoring. Trustible maps every agentic use case to applicable obligations, including EU AI Act classification, and generates the documentation they require — updating mappings as EU AI Office guidance on agentic systems develops.
What comes next
Related solutions
Agentic governance builds on the same structured foundation as the rest of your AI program.
Solution
Accelerate AI Intake
The structured intake behind every agent registration — purpose-built questions, risk-based routing, and workflow orchestration from submission to decision.
Explore
Solution
AI Risk and Impact Management
The risk scoring engine and mitigation library powering agentic assessment — dedicated agentic attributes, irreversibility scoring, and accountability documentation built in.
Explore
Solution
Continuous AI Monitoring
Structured oversight after agent approval — periodic reviews, attestations, and modification tracking so agents stay within their authorized scope.
Explore
See it in your environment
Your agents need governance before they act.
Trustible gives you structured intake, risk assessment, and audit trails built specifically for autonomous AI systems.
Live in 30 days
No MLOps required
Expert team included
SOC 2 certified