Use Case · Agentic AI Governance

Govern what AI agents can do on your behalf.

AI agents now act across your enterprise systems: moving data, onboarding vendors, renewing contracts, committing spend. Identity tools answer which systems an agent can reach. They do not answer the question governance turns on: what is the agent authorized to commit the company to, up to what limit, and when must it escalate to a human? That is an authority question, and it governs machine and human decision-makers in one matrix. Aptly governs it, with a full audit trail of every action.

Aptly evidence of who could approve capital commitments in Q2, with SAP-enforced limits, mapped to SOX, APRA CPS 230 and the UK Code.
The Gap

Knowing which systems an agent can reach is not the same as governing what it can commit you to.

Most enterprises can grant an AI agent access. Far fewer can state, and prove, what that agent is authorized to decide on the company's behalf, up to what limit, and when it must hand off to a human. That gap is where autonomous systems quietly exceed their mandate.

Access is governed; authority is not. Identity and access tools decide which systems an agent can log into. They are silent on the decision the agent then makes inside those systems: the renewal it commits to, the vendor it onboards, the refund it issues. Reachability is not authorization.

Agents act faster than oversight can keep up. A human approver is a natural checkpoint; an agent executing thousands of actions a day is not. Without an enforced limit and a defined escalation point, "the agent did it" becomes the after-the-fact explanation for a commitment no one approved.

Machine and human authority live in separate worlds. The board-approved authority matrix governs people; agent permissions live in code, scattered across tools and teams. No single record answers "who, or what, was authorized to commit us to this, and was it within limits?"

44%

Third-Party Research

In a 2025 survey of 353 organizations by Dimensional Research for SailPoint, 82% already used AI agents, but only 44% had formal policies in place to govern them. That gap, between deploying agents and governing them, is exactly what an enforced authority layer closes.

Survey-based; Dimensional Research for SailPoint, 2025, n=353.

The fix is not tighter credentials. It is governing the agent's decision authority the same way you govern a person's: with limits, conditions, escalation, and evidence.

The Authority Layer

One authority layer. Humans and agents, governed the same way.

Aptly sits between your identity systems (Okta, Microsoft Entra ID, SailPoint) and your execution systems (SAP, Oracle, NetSuite, Workday, ServiceNow) as the single source of truth for who, and what, can approve, sign, and commit on behalf of the enterprise. An AI agent is modeled as a principal in the same authority matrix as your people: scoped to specific decision types, bounded by limits and conditions, required to escalate to a named human above its ceiling, and recorded on every action.

Because Aptly publishes that authority layer over its REST API and an MCP endpoint, across 30+ connected systems, an agent can check in real time whether a proposed action is within its authorized scope before it acts, and the request is recorded against the authority that governs it. The agent does not have to guess at its mandate, and the company does not have to reconstruct it later.

Identity systems
Who can log in
OktaMicrosoft Entra IDSailPoint
The Authority Layer
Aptly governs who, and what, can approve, sign, and commit
Delegations, limits, conditions, and signatories, versioned and evidenced.
Execution systems
Where transactions happen
SAPOracle · NetSuiteWorkday · ServiceNow
This is decision authority, not agent identity or credentials.

Machine-identity and non-human-identity tools answer which systems an agent can reach and whether its secrets are secure. Aptly answers what it is authorized to commit the company to, up to what limit, and who is accountable. Identity governs the door; Aptly governs the decision once the agent is through it.

How It Works

From agent scope to accountable action, in four steps.

1
Define what the agent may do, and where it stops.
Capture the agent's authority as a structured record: which decision types it can act on, its limit (for example, SaaS renewals up to a set amount), the conditions attached (approved-vendor list, term length, geography), and the named human it escalates to above any of them.
2
Scope it in the matrix, alongside your people.
Place the agent on the same authority ladder as human approvers, deliberately bounded tighter than any human tier, so machine and human decision rights are governed by one policy, not two.
3
Enforce with human-in-the-loop escalation.
Within scope, the agent acts and the action is logged; before it acts, it can confirm authorization against Aptly over REST or MCP. Above its limit, or outside its conditions, Aptly halts the agent and routes the decision to the accountable human automatically, with full context.
4
Capture the full audit trail.
Every agent action, approved in scope or escalated, is recorded with the agent's identity, the decision, the limit it was tested against, the outcome, and a timestamp. Evidence of who, or what, was authorized is a by-product of running the system.

Define what the agent may do once, enforce it on every action, and the audit trail accrues by itself. Governed autonomy becomes a property of the system, not an after-the-fact reconstruction.

The Platform

The platform behind governed autonomy.

Delegation of Authority
Available
Model an AI agent as a principal with explicit limits, conditions, and a named escalation point, on the same authority matrix as your people, deliberately bounded tighter than any human tier.
Learn more →
Authority Hub
Available
See and track every authority across the enterprise, human and agent, from one dashboard across 30+ connected systems over REST and MCP, with immutable action and audit logs on every decision.
Learn more →
Intelligence
In Preview
Give agents and the people who oversee them policy-aligned answers on what they may decide and when they must escalate, with every guidance request and action tracked against the authority that governs it.
Learn more →
Signatory Management
Available
Keep validated signatory authority behind any binding commitment, so an agent never executes a contract the company has not authorized a signatory to bind.
Learn more →
See it govern your AI agents against a framework you answer to.
Book a Discovery Call
Frameworks

The oversight controls regulators and standards now expect.

Human oversight of automated decisions, defined limits, and a record of how the system operated are becoming explicit obligations. Aptly maps your agent-authority evidence to the frameworks that ask for it.

EU AI Act

Keep human oversight of automated decisions.

Providers and deployers of high-risk AI must ensure effective human oversight and keep records of how the system operated. Authority over what an AI agent may commit the company to is an oversight control; the audit trail is the record.
In force since Aug 2024. Under the Digital Omnibus agreed May 2026 and pending adoption, some high-risk obligations are deferred to 2 Dec 2027; transparency obligations apply from 2 Aug 2026. Evolving; subject to change.
NIST AI RMF

Show clear accountability over AI systems.

The Govern and Manage functions call for clear roles, accountability, and risk controls over AI systems. Defined authority and an audit trail are how that accountability is evidenced.
AI RMF 1.0, Jan 2023; Generative AI Profile AI 600-1, Jul 2024; voluntary framework.
ISO/IEC 42001

Operate a certifiable AI management system.

The certifiable AI management system standard requires assigned accountability, human-oversight controls, and ongoing monitoring across the AI lifecycle. Aptly's structured agent-authority records and action logs are the evidence an audit looks for.
ISO/IEC 42001:2023.

Aptly governs the agent's decision authority, not its identity. Machine and non-human identity tools secure the agent's credentials; Aptly complements that layer rather than replacing it. Standards bodies are converging here: NIST opened work on AI-agent identity and authorization in early 2026, and OWASP's 2025 agentic security work names excessive agency, an agent acting beyond its intended scope, as a leading risk.

You scope an agent's authority once. Each framework reads the same evidence in its own language, so a new obligation becomes a mapping exercise, not another build.

Proof

What governed autonomy looks like.

At Meridian Industries, the CIO owns agent onboarding; no agent acts on the company's behalf until its authority is defined in Aptly. The procurement agent the team scoped handles SaaS renewals up to $50K ACV, capped by four standing conditions: no more than a 5% price uplift, terms of 12 months or less, vendors on the approved list, EU only.

“The agent operates inside a narrower envelope than any human, by design.”
In-scope renewal, logged
$32K ACV at 4.2% uplift, approved vendor. Renewed inside scope, reference MER-2026-00224.
Over-ceiling, escalated
$90K ACV halted and routed to the CIO with full context.
One audit trail
Principal, limit tested, outcome, and timestamp, for every action, human or agent.

The daylight is deliberate. The agent's $50K ceiling sits well below the Director rung ($250K) on Meridian's authority ladder. Both the in-scope renewal and the escalation sit in one audit trail, the same one that governs every person on the matrix.

Scenario based on Aptly's canonical Meridian Industries dataset.
FAQ

Questions teams ask about governing AI agents.

What is the difference between AI agent authority and AI agent identity?
Identity governs which systems an agent can reach and whether its credentials are secure: the job of machine-identity and non-human-identity tools. Authority governs what the agent is allowed to decide once it is there: the limit it can commit to, the conditions it must meet, and when it must escalate to a human. Aptly governs the authority and complements your identity stack rather than replacing it.
What can an AI agent be authorized to do?
Whatever you scope it to, and nothing beyond. In Aptly an agent is a principal with explicit decision types, a limit, attached conditions, and a named human it escalates to above that limit. A procurement agent might renew approved-vendor SaaS up to a set amount; anything larger, off-list, or longer-term routes to a person.
How does an agent know its limits before it acts?
It can ask. Aptly publishes the authority layer over its REST API and an MCP endpoint, so an agent can confirm in real time whether a proposed action is within its authorized scope before it commits, and the check is recorded against the authority that governs it. Within scope, the agent proceeds; above its limit or outside its conditions, Aptly escalates to the accountable human.
How does Aptly help with the EU AI Act, NIST AI RMF, and ISO/IEC 42001?
All three expect human oversight, clear accountability, and a record of how an AI system operated. Aptly holds the agent's authority as structured records and logs every action it takes, producing the oversight evidence those frameworks look for. (These frameworks are evolving; see the dated note above.)
Can Aptly govern human and machine decisions in one place?
Yes. Agents and people sit on the same authority matrix, governed by one policy with one audit trail, so “who, or what, was authorized to commit us to this?” has a single answer.
Pairs With

Built to work with the rest of your authority program.

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Continuous Authority Assurance
Monitor agent and human authority continuously, with daily proof instead of a quarterly reconstruction.

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Multi-Entity Governance
Govern agent and human authority consistently across every entity and jurisdiction.

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See your AI agents governed like everyone else who can commit you.

Bring an agent you are deploying, whether procurement, finance, or operations. We will show you how Aptly scopes its authority, enforces escalation above its limit, and logs every action, on the same matrix as your people.