AI agent management: Run agents like a team

AI agent management is the practice of assigning work to AI agents, setting limits on what they can do, checking their output and deciding which agents deserve to stay in your operation. If you run a small business or lead a lean team, the aim is to get useful work completed without creating another layer of supervision that consumes your day. An agent needs a defined job, a budget, rules for when to stop and a way for you to inspect what happened.

Managing agents calls for some of the same habits you use with people, but the details differ. An agent does not understand a vague request through office context, notice that a colleague is having a difficult week or take responsibility for a poor decision in the human sense. You have to make the boundaries explicit. We recommend four regular practices: write a brief before assigning work, set a hard spending limit, review the activity log every week and retire agents whose work no longer justifies their cost or oversight.

Give each agent a job you can describe in one sentence

A team member with an unclear job will spend time asking what matters. An AI agent with an unclear job may produce a convincing answer to the wrong question. Before you add an agent to your operation, write down the task it owns and the result you expect. 'Help with customer support' is too broad. 'Sort incoming support messages by issue, identify urgent cases and draft a reply for review' gives the agent a job you can inspect.

The brief should explain the source material the agent may use, the format of the deliverable and the conditions under which it must stop and ask you a question. Include examples of acceptable work where possible. If the agent is preparing a competitor memo, specify which competitors to cover, what counts as a verifiable finding and how to label missing information. A missing figure should remain missing rather than being filled with a plausible estimate.

Keep the job narrow until you understand its failure modes. A research analyst can prepare a draft with source references before receiving permission to touch another system. An inbox triager can sort messages and suggest replies without sending them. The separation matters because a useful draft and an authorised external action are different things. You should be able to explain what the agent owns without giving it authority over every step that follows.

Set a budget before the work starts

People rarely get an unlimited expense account for an assignment, and agents should not receive unlimited room to run either. Set a hard budget for each delegation before it begins. The limit should reflect the value of the task, the amount of source material involved and how much investigation the work reasonably needs. A small inbox sorting job does not need the same allowance as a research assignment covering several markets.

A budget is a stopping rule, not just a way to record spending afterwards. If an agent reaches its cap before finishing, you can inspect the partial result and decide whether to approve more work, narrow the brief or stop. This is preferable to letting a run continue because it has already spent time on the task. Work already completed does not automatically justify spending more.

Treat repeated overruns as a management signal. The brief may be too broad, the source material may be poor or the task may be a bad fit for an agent. Compare the expected effort with what the log records, then change the next assignment accordingly. If you cannot explain why a task needs more budget, do not raise the limit simply to get a finished deliverable.

Keep external actions behind a human decision

An agent can prepare work inside your workspace without receiving permission to carry out every action that follows. Sending an email, publishing a page, paying an invoice or changing a business record can affect people and systems beyond the assignment. These actions need a separate decision because a good draft does not prove that the timing, recipient or consequences are right.

Build approval into the way you manage the agent rather than relying on a general instruction to be careful. In Delagents, actions that affect anything outside the workspace wait in an approvals inbox. Each approval card shows the exact proposed action, the cost so far and a deadline. A person can approve it, change it or reject it. The system also requires human approval for first contact with someone outside the workspace, moving money or issuing refunds, deleting or overwriting records the agent did not create, changing roles or its own policy, connecting integrations or widening their scopes, accepting terms or signing, publishing publicly and acting in another workspace.

For daily management, look at the action itself rather than approving a batch because the agent has completed its assignment. Check the recipient, amount, destination and relevant record. If an outreach draft is sound but the recipient is wrong, change or reject that action. If the evidence behind a proposed refund is incomplete, send the task back for more information. A checkpoint is useful only if the person reviewing it has enough detail to make a decision.

Make the weekly log review a short working meeting

A weekly review gives you a chance to catch small problems before they become the normal way an agent works. Set aside 10 to 15 minutes for each group of related delegations, or review the agents together if the roster is small. Look at the activity log, the deliverables and the decisions that required your attention. In an append-only log, earlier records remain available rather than being silently replaced, so you can examine the sequence of tool calls, approvals and policy changes.

Start with exceptions. Did the agent stop because it lacked information? Did it repeat a tool call, reach its budget cap or return a result that needed substantial correction? Did you reject an action because the brief failed to state a boundary? Those events tell you more about the next improvement than a simple count of completed assignments.

Choose one concrete change after the review. You might add an example to the brief, remove an unnecessary source, lower the budget on a predictable task or require a checkpoint before a sensitive step. Then check the next run against that change. Avoid rewriting the entire setup after one imperfect result. The purpose is to correct a known weakness and see whether the correction works.

Keep the review tied to evidence. A polished deliverable can conceal unsupported claims, and a failed run can still reveal that an agent stopped at the right time. Judge the work, the use of resources and the decisions it asked you to make. That record gives you a better basis for managing the roster than impressions about which agent seems most capable.

Retire agents that no longer earn their place

An agent should not remain on the roster simply because someone took time to configure it. Jobs change. A process may disappear, a source may become unavailable or the work may turn out to need human judgement at every stage. Keeping an agent active in those circumstances adds another configuration to maintain and another source of activity to review.

Before retiring an agent, examine its recent deliverables and the time you spend correcting or supervising them. Ask whether the output is still useful, whether another agent already covers the task and whether a narrower brief would solve the problem. A weak run may call for a repair. A repeated mismatch between the job and the agent's capabilities may call for retirement.

Record the reason for the decision, along with any useful instructions or examples from the agent's previous work. That gives you a reference if the task returns later. Also check whether any scheduled work, open delegations or pending approvals need attention before you remove the agent from active use. Retirement should close out the responsibility cleanly, not leave unfinished work for someone else to discover.

Review the roster as a working list of responsibilities, not a collection of interesting experiments. Delagents provides an agent roster and builder for choosing and configuring roles such as a research analyst, support triager, bookkeeper or content repurposer. Use the roster to make each agent's purpose clear, then apply the same review standard to every role, including the ones you built yourself.

Start with one agent and a review date

Pick one recurring task that has a clear output and limited consequences if the draft needs correction. Write a brief with a defined deliverable, approved sources and explicit stop conditions. Set a hard budget, decide which actions require your sign-off and schedule a weekly review of the activity log. Do not expand the assignment until you have inspected enough runs to understand where it works and where it needs help.

At the end of the first review, make a decision you can act on: keep the assignment as written, revise one part of it or stop using the agent for that job. Put the review date on your calendar now, before you start the delegation. That single appointment turns AI agent management from an occasional reaction to a routine you can maintain.