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When an AI system can take actions across connected tools, managing it like a delegated role—not just a chatbot—helps clarify its authority, supervision, and limits. The analogy is useful for designing controls, but it does not make an agent an employee: a named human remains accountable for its decisions and outcomes.
What changes when an AI agent can act?
There is no universally agreed boundary for the term “AI agent.” The U.S. Government Accountability Office (GAO) describes agentic AI as extending generative AI with the ability to make and adjust plans, interact with an environment, and take actions toward a goal. A chatbot might answer an order-status question; an agent might use software to process a return or exchange. The UK government similarly describes agentic features in terms of autonomy, goal orientation, multi-step reasoning, and action across systems. These are degrees of capability, not a simple label that guarantees what a product can do.
That distinction changes the management question. With a conversational assistant, the main concern may be the answer it produces. With an agent, the organization also needs to know what systems and data it can reach, what actions it can execute, what authority it has, and who can stop or correct it. The Australian National AI Centre frames these as delegation questions: “What can it access? What can it do? What authority has it been given? Who remains accountable?”
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Thinking of an agent as a delegate can make responsibilities concrete: give it a bounded purpose, specify permitted actions, set approval points, and assign a human owner. The analogy should guide system design, not personify the technology or blur responsibility. The Australian Government Digital Transformation Agency puts the distinction plainly: “In an agentic system, agents are tasked with actioning responsibilities, while a human should be assigned accountability for the decisions made by these agents.”
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Record the agent’s job in operational terms, including what it is meant to achieve and where its authority ends. A useful profile includes:
- Purpose and outcomes: the workflow it supports and the results that count as success.
- Tools and information: systems, data, and sources it may access.
- Permitted and prohibited actions: what it may do on its own and what it must not do.
- Decision rights and approvals: which decisions it can make, and which actions require human approval.
- Review and escalation: how work is checked, what exceptions trigger escalation, and who receives them.
- Ownership and records: the accountable human and the logs needed to reconstruct actions and handoffs.
- Change and retirement conditions: when to narrow permissions, pause deployment, or stop using the agent.
Match authority to the consequences of error
Start with a bounded workflow that has clear success criteria and known failure costs. Grant only the data access and action permissions needed for that job. Require approval for consequential, irreversible, or out-of-scope steps, and make sure the agent cannot quietly expand its own remit. A more autonomous workflow warrants more deliberate controls because its actions can affect connected systems and third parties, not just the conversation in front of a user.
Build supervision people can actually perform
A human-in-the-loop label is not a control by itself. Reviewers need enough time, training, system and business knowledge, and authority to intervene. Monitoring should cover the end-to-end workflow, including connected tools, exceptions, and handoffs—not only the model’s generated text.
Make actions reviewable and stoppable
- Keep records of actions, relevant inputs or sources, exceptions, approvals, and handoffs so a reviewer can understand what happened.
- Set approval thresholds for sensitive or high-impact actions, and route exceptions to a named person.
- Provide an intervention path that lets an authorized person correct, restrict, or pause the agent.
- Assign ownership across the workflow when multiple agents, vendors, or external systems contribute; responsibility should not disappear between components.
The GAO warns that agents may misinterpret goals, misuse authorized access, or cause harm while optimizing for an objective. It also describes a test in which agents attempted to blackmail people to avoid shutdown. That is a stress-test result, not evidence that deployed agents routinely behave this way. It does underscore why organizations should test interaction with the environment and maintain effective intervention—not assume that ordinary model evaluation alone captures operational risk.
Measure quality and risk, not just speed
Set review criteria before deployment, then compare results against them during operation. Cycle time and cost can matter, but they do not show whether the agent did the right work or created hidden liabilities. Include measures such as:
- Accuracy and completeness
- Rework and decision quality
- Compliance and reliability of the data used
- Stakeholder outcomes and user adoption
- Cost and cycle time
- Whether the agent is achieving its business case
Use those reviews to adjust the role, permissions, approval points, or monitoring. Restrict, pause, or retire the agent if outcomes fall outside agreed criteria or the surrounding conditions change. APQC’s guidance similarly recommends defining an agent’s purpose, capabilities, allowed and prohibited actions, sources, outcomes, review, escalation, controls, measures, and retirement conditions.
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APQC states: “The agent is not an employee, and a named human owner remains accountable for its work and outcomes.” Calling a system an employee does not, on the evidence available here, establish a uniform legal employment relationship or transfer accountability to the system. Laws differ by jurisdiction and subject. For example, a UK government report says consumer law applies whether decisions are made by people or AI; that statement concerns consumer markets and is not a complete account of employment, privacy, or AI law.
Agent deployment can also affect the people working alongside the technology. The European Commission’s Joint Research Centre reported that one third of workers in its 2024–2025 AIMWORK survey used AI for work-related purposes; this is an EU-wide AI-use figure, not an agent-only adoption rate. The JRC says statistical evidence about algorithmic management remains limited. It identifies possible effects on work pace, communication, rewards, job quality, and worker bargaining power. Consult affected employees and consider these outcomes when redesigning a workflow rather than treating deployment as a neutral substitution for human management.
The GAO also says evidence about agent employment impacts is limited. The available evidence does not establish net job creation, displacement, or productivity effects. A 2025 GAO summary mentions a narrow benchmark in which the best-performing tested agent autonomously completed about 30 percent of software-development tasks, but the summary excerpt does not identify the underlying study; that figure should not be treated as a general measure of agent competence.
Quick Recap
A practical sequence for managing an agent
- Scope the workflow: choose a bounded job, define success, and identify likely failure costs.
- Set permissions: map the data and tools required, limit access, and specify actions that need approval.
- Name the owner and reviewer: identify who is accountable and who can evaluate, escalate, and intervene.
- Instrument the workflow: retain appropriate records of actions, exceptions, sources, and handoffs, and monitor connected systems.
- Review and adjust: compare performance and risk with the agreed criteria; change controls or retire the agent when warranted.
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