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Federal agencies should give an AI agent only the authority required for a defined task, bind that authority to a known agent and accountable human sponsor, enforce it at the systems the agent uses, and keep verifiable records of its actions. A graduated “authority ladder” can help agencies decide which actions an agent may take, which require human approval, and which are off limits. The ladder is a proposed policy model—not an adopted federal standard.
Why do AI agents need an authority ladder?
An AI agent can make decisions and take actions across connected tools with limited human supervision. That makes its permissions consequential: an agent that can read a record may also be able to alter it, send it elsewhere, or trigger a process. Agencies need a way to match those permissions to the task and the potential impact of an error.
NIST’s February 2026 draft concept paper describes agents as systems capable of autonomous decision-making and action, and asks how identity and access-management principles can help ensure agents are known, trusted, and governed. It raises questions about least privilege, delegated authority, human-agent identity binding, and verifiable logs. The paper is a draft for stakeholder input, not a binding standard.
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The practical test is not whether an agent has been instructed to behave responsibly. It is whether the agency has defined what the agent is authorized to do, tied that authority to an accountable sponsor, and made the systems and resources the agent calls reject actions outside that scope.
What should the authority ladder look like?
The following five levels are an editorial proposal based on identity, authorization, delegation, human oversight, and limits on autonomy discussed by NIST and CISA. They are not a five-level NIST or CISA framework. Set an agent’s level according to the action’s impact, the sensitivity of the data or system, and how difficult it would be to reverse the result.
1. Read approved information
The agent may retrieve information from specifically approved sources, but cannot change records or initiate an external action. Specify which data and tools are in scope; “read-only” should not mean unrestricted access to agency information.
2. Draft or recommend
The agent may prepare a response, analysis, or proposed change, but a person or separately authorized process must decide whether to use it. The agent’s output is not itself permission to act.
Rank #2
3. Make bounded, reversible changes
The agent may perform defined changes within a named system when the action is limited in scope and can be undone. Set the allowed operations and relevant constraints in advance rather than relying on the agent to infer them.
4. Take designated consequential actions with approval
For selected sensitive or consequential actions, require approval from a designated human before the system executes the action. The approval should identify what is being approved and apply to that action—not silently authorize a wider set of future operations.
5. Never take prohibited actions
Some actions should remain unavailable to the agent under the applicable policy, regardless of a request it receives or an apparent benefit. Enforce prohibitions through permissions and system controls, not just instructions in a prompt.
Rank #3
How should an agency assign and enforce an agent’s authority?
Write the authorization as a bounded grant attached to an identifiable agent and a responsible sponsor. The grant should make clear what task it covers and which systems, data, and actions are permitted. A useful implementation should also make changes to the grant, expiration, and revocation operationally effective.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errors- Give the agent a distinct identity. Systems should be able to distinguish an agent from a human user and associate it with an accountable agency sponsor.
- Limit scope. Apply least privilege to the agent’s tools, data, and allowed operations for the particular task. Do not grant broad access merely because it is convenient for a general-purpose agent.
- Make delegation traceable. When the agent acts on someone’s behalf, retain evidence of the human or agency authorization behind the action. The agent should not be treated as the source of its own authority.
- Enforce at the boundary. Tool, API, and resource controls should check whether an action is authorized. Instructions to the model can help shape behavior, but should not substitute for access decisions made by the systems it calls.
- Record the chain of action. Logs should let an agency connect the agent identity, sponsor or delegated authority, requested action, relevant data sources, approval where required, and outcome. NIST’s draft raises verifiable logging and transparency as areas to explore.
NIST’s concept paper asks, among other things, how organizations can establish proof of authority for specific actions and determine whether users are authorized to access an aggregated response produced by an agent using additional tools and resources. Those questions matter because an agent’s ability to combine information or call a tool does not establish that the resulting access or action is permitted.
When should a human approve an action?
Human approval is most useful when it changes the authorization outcome for a defined class of actions. Agencies can reserve approval for consequential, sensitive, or otherwise higher-impact actions while allowing lower-impact work to proceed within a bounded grant. Neither the cited guidance nor the proposed ladder implies that a person must approve every agent action.
For an approval gate to be meaningful, the system should hold the action until an authorized person approves it, and the approval should be tied to the proposed action. If an agent can proceed through another tool or route around the gate, the review is not an effective control. Agencies should also make clear which role can approve, what information the approver needs, and what happens when approval is denied or unavailable.
How does this fit current federal AI governance?
OMB Memorandum M-25-21, issued in February 2025, provides broader governance context. It directs agencies to empower accountable AI leaders, establish appropriate safeguards, and maintain risk-management practices for high-impact AI, subject to the memorandum’s scope and exceptions. It also says AI risk acceptance is separate from, and does not supersede, the authorization process for information systems. The memorandum does not define a technical authority ladder for autonomous agents.
NIST’s February 2026 draft concept paper describes a possible NCCoE demonstration applying existing identity standards and practices to agents. It proposes exploring identification, authorization, delegated access, logging and transparency, and data-flow provenance; these are areas for exploration, not finalized NIST requirements. Separately, NIST announced its AI Agent Standards Initiative on February 17, 2026, organized around industry-led standards, community-led open-source protocols, and research on agent security and identity. The announcement said further guidelines and deliverables would follow.
On May 1, 2026, CISA and five international partner agencies announced joint guidance on agentic AI. CISA’s announcement summarized its recommendations as “Limiting agent autonomy by ensuring agents are not granted broad or unrestricted access—especially to sensitive data or critical systems.” It also described identity management, layered defenses, oversight, threat modeling, monitoring, and regular assessments as relevant security practices.
A January 8, 2026 Federal Register request for information from NIST notes that agent systems may include multiple subagents and can operate with little or no human oversight. It identifies risks including indirect prompt injection, data poisoning, backdoors, and specification gaming. Those are risks to consider in threat modeling, not evidence that every deployed agent exhibits those behaviors.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should an agency check before deployment?
Use the authority model as a design and review aid, not as a substitute for applicable law, policy, or information-system authorization.
- Can the agency identify the agent separately from its human sponsor?
- Is the task, permitted data, available tools, and allowed action scope explicit?
- Does each delegated action retain a traceable link to the authorizing human or agency role?
- Do the systems the agent calls enforce the permission limits and approval gates?
- Can the agency reconstruct what the agent did, what information it used, and what result followed?
- Does threat modeling cover indirect prompt injection, privilege escalation, and other relevant threats, with monitoring and reassessment as the system changes?
These checks address different parts of the problem: governance assigns responsibility, identity and authorization establish who may act and within what scope, and security controls constrain and monitor execution. No single layer makes an agent risk-free.
What do federal AI governance counts tell us—and not tell us?
GAO reported 94 AI-related requirements with government-wide scope or implications and 10 executive-branch oversight and advisory groups involved in federal AI implementation and oversight as of July 2025. Those figures describe the broader federal AI governance landscape. They are not counts of agent-specific rules, agent deployments, or agent-related failures, and they do not measure whether an authority ladder reduces risk.
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