Work with an AI agent by assigning a bounded outcome, limiting its access and authority, setting approval points for consequential actions, and checking the evidence and changes before you accept the result. The more harmful or difficult to reverse an action could be, the more oversight it needs. That is a practical way to manage agentic work—not a universal standard, and not a guarantee that every product offers the controls described here.
What makes an AI teammate agentic?
An agentic system can direct its own process and tool use toward a goal rather than merely following a fixed sequence of instructions. Anthropic describes the work as a loop: plan, act, observe, adjust, and repeat until the task is done or human input is needed. OpenAI’s 2023 governance paper describes agentic AI systems as able to pursue complex goals with limited direct supervision.
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The label alone does not tell you how much freedom a particular system has. Before delegating, find out what it can access, what it can change, whether it can affect other people or external services, and whether you can interrupt or reverse its actions. A system that can search internal documents is not equivalent to one that can also send messages, change permissions, or submit transactions.
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How to assign work to an AI agent
Describe the outcome, not just the activity
State what result you need and who or what it is for. “Review these support tickets and identify recurring causes of delays” is a more useful assignment than “analyze the tickets.” Give the agent the context needed to interpret the goal, the constraints it must respect, and the form the deliverable should take.
- Goal: What should be true when the work is complete?
- Context: Which documents, systems, time period, or definitions matter?
- Constraints: What must it not disclose, change, assume, or decide?
- Deliverable: What output should it produce, and what evidence should accompany it?
- Completion criteria: How will you judge whether the result is usable?
- Stop conditions: Which uncertainty, conflict, or proposed action should make it pause and ask?
Ask it to surface material assumptions rather than silently choosing between plausible interpretations. This helps reduce the risk of an agent misreading intent, a concern Anthropic identifies in its April 9, 2026 discussion of trustworthy agents.
Set authority before the agent starts
Specify what information and tools the agent may use, then distinguish access to inspect from authority to modify or act. For example, it might be allowed to read a project folder and draft a status update, but not edit the folder or send the update. Give it the narrowest access that still lets it do the assigned job.
| Authority | What it permits | Example boundary |
|---|---|---|
| Read | Inspect approved information without changing it | Summarize selected project documents; do not edit them |
| Draft | Prepare a proposed change or communication for review | Write a customer response; do not send it |
| Modify | Change an approved file or system | Update a designated draft; stop before publishing |
| Execute externally | Send, submit, purchase, delete, or otherwise affect an external system or person | Require a human decision before the action |
These are practical categories, not a standard permission model. Products differ, and some may not let you separate these capabilities cleanly. NIST’s NCCoE notes that traditional identity and access management approaches may not fully address autonomous agents; its identity and authorization project is developing implementation-oriented guidance.
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Choose checkpoints based on two questions: how much harm could an action cause, and how easily could you undo it? OpenAI’s Operator system card describes a product-specific policy that considered potential harm and reversibility, with oversight and explicit confirmation for certain actions. That example should not be mistaken for a rule followed by every agent.
| Work or action | Practical checkpoint | Why |
|---|---|---|
| Low-impact, reversible analysis or a draft | Review the result before relying on it | An error is usually contained in the draft or analysis |
| Changes to shared files, records, or settings | Inspect the proposed change; approve before applying when feasible | The change may affect other people or be inconvenient to restore |
| External messages, financial actions, deletions, permission changes, or consequential decisions | Require a human checkpoint before execution | The effects may be difficult to reverse or materially harmful |
Match the checkpoint to the actual action, not to the vague reassurance that a person is “in the loop.” Decide what the person must inspect, what they are approving, and whether the agent is blocked until approval. If a system cannot pause before a high-consequence action, do not grant it authority to take that action.
How to monitor, stop, or redirect an agent
Make progress visible enough to judge whether the agent is following the assignment. Useful visibility can include its plan, the sources it is using, consequential actions it proposes, and points where it is uncertain. Anthropic’s August 4, 2025 framework describes plans and user redirection as examples of transparency, while noting that too little detail can make assessment difficult and too much can overwhelm people.
- Check what the agent plans to do next when the task is consequential or unfamiliar.
- Ask it to explain the evidence behind an important intermediate conclusion.
- Pause it when the scope changes, an unexpected permission is requested, or a stop condition occurs.
- Redirect it by restating the goal or narrowing the allowed actions, rather than allowing it to improvise around a boundary.
Visibility is useful only if someone can act on it. Know how to pause the run, revoke access, or prevent the next external action in the particular system you use. Interface controls vary; do not assume a product has a universal stop button or that stopping a run reverses changes already made.
How to review an agent’s work
Review the result against the original assignment, not just its fluency or confidence. For work that matters, inspect both the deliverable and the evidence or changes behind it.
- Check the goal: Does the output meet the stated completion criteria and answer the intended question?
- Verify material claims: Check important facts against the underlying sources, especially where a mistake could affect a decision.
- Inspect actions: Review files, records, messages, or settings the agent changed or prepared to change.
- Check authority: Confirm it used only permitted data and tools and did not cross a stated boundary.
- Resolve uncertainty: Correct the work, ask for supporting evidence, or escalate the decision if material uncertainty remains.
NIST’s AI Risk Management Framework is voluntary guidance for incorporating trustworthiness into AI design, development, use, and evaluation. It supports treating evaluation as part of responsible use, but the cited framework page does not prescribe a single checklist for reviewing agent output.
What to do after a boundary or quality failure
If an agent misunderstands the goal, tries an unauthorized action, encounters adversarial content, or leaves a difficult-to-reverse result, do not simply repeat the task with the same setup. Identify which part of the assignment or operating boundary failed, then adjust it before similar work:
- If the goal was ambiguous, sharpen the outcome, constraints, or completion criteria.
- If access was broader than necessary, remove unneeded data or tool permissions.
- If an action happened too early, add an approval checkpoint before that action.
- If the result is hard to inspect, request evidence, a change summary, or more visible progress.
- If suspicious content or tool behavior appears, pause and investigate before granting access again.
Anthropic discusses prompt injection, tool vulnerabilities, and information crossing contexts as agent risks. NIST’s NCCoE identifies data leaks, prompt injection, compliance failures, and unpredictable behavior among risks associated with weak identity, authorization, and governance. These sources identify categories of concern; they do not establish a universal failure rate or a single control that prevents every incident.
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What current AI governance guidance does—and does not—establish
NIST AI Risk Management Framework
NIST describes the AI RMF as voluntary guidance for incorporating trustworthiness into AI design, development, use, and evaluation. Its official page says AI RMF 1.0 is being revised. NIST released its Generative AI Profile, NIST-AI-600-1, on July 26, 2024. The cited material does not make the framework an agent-specific binding standard.
NIST AI Agent Standards Initiative
NIST’s initiative page, updated August 14, 2026, lists work on voluntary guidelines, interoperable agent protocols, agent authentication and identity infrastructure, and security evaluations. This is active standards work, not evidence that a completed universal agent-governance standard is already in force.
NIST NCCoE identity and authorization project
The project aims to produce implementation-oriented material and an SP 1800 series practice guide. Its introduction reports that NIST published a concept paper in February 2026 and received over 600 responses. That figure is NIST’s reported response count; it does not establish consensus or mean a final practice guide is available.
OpenAI’s governance paper
OpenAI’s paper, published December 14, 2023, proposes lifecycle responsibilities and baseline practices for agentic systems. It also describes unresolved operational questions that would need to be addressed before practices could be codified. Treat it as a proposal, not a universal operating requirement.
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Together, these sources support practical attention to authorization, oversight, evaluation, and security, but they do not prescribe one universal workflow or prove that every agent product implements these controls. Apply the principles to the capabilities and risks of the system you actually use.
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