The loop in four moves
- Observe: read the request and the results already available.
- Decide: select the next permitted step.
- Act: search a knowledge source, call an API, draft a reply, or request approval where configured.
- Verify: inspect the response, then finish, retry within limits, or hand off.
OpenAI’s Agents SDK runner describes the same shape: it runs tools, adds results to context, and runs again until there is a final answer. The ReAct paper made the pattern explicit by interleaving reasoning with actions and observations.
A support-ticket example
A customer asks where an order is. The agent first identifies the request, retrieves the relevant policy, checks whether an approved order-status tool is available, and reads its response. If the order lookup says the shipment is delayed, the agent can explain the delay using the policy. If the lookup fails, the ticket contains a refund request, or the policy does not cover the case, the correct next step is handoff, not a confident guess.
That is why an agent loop is not synonymous with autonomy. Its quality comes from what it may access, what it must prove, and when it stops. See also AI agents versus chatbots.
Put controls around every loop
Use a maximum number of steps, a cost budget, a no-progress rule, and approval for consequential actions. Log the input, tool call, result, and final outcome. Test difficult cases such as missing knowledge, conflicting policies, and a tool timeout. These controls make a failure diagnosable instead of invisible.
Operate a support teammate from the terminal
eesel CLI is a terminal route to the same teammate and workspace shown in the dashboard. With Node.js 18.17 or newer, npx @eesel/cli is the same CLI as eesel. A support lead can inspect the current setup; a script or Claude Code, Codex, or Cursor can do the same JSON-based inspection.
For an order-status loop, first inspect the teammate's permitted sources and instructions. Configure the test's order lookup as read-only and disable unrelated outgoing actions before a redacted “where is my order?” case; the CLI does not impose that boundary automatically. Set an explicit retry rule for this example: a timeout permits one retry, while no matching order requires clarification or handoff. Neither result justifies an invented delivery date:
npx @eesel/cli integrations --agent "Support teammate"
npx @eesel/cli integrations download list --agent "Support teammate"
npx @eesel/cli instructions --agent "Support teammate"
Review the output with the team, use --dry-run before an approved change, and test in a new named session. Compare the result to the expected retry and handoff. The CLI can also list integrations, check custom API access, show automations, and list approvals; dashboard and terminal operate the same teammate.
Try a controlled support loop
The eesel Zendesk integration page lists connected sources and triggers beside the teammate chat panel.
If your goal is a bounded agent loop for a support queue, eesel’s AI helpdesk teammate works with the helpdesk already used by the team. Start by inspecting its knowledge, instructions, integrations, and approvals, then test the scenario you care about before expanding its scope. Try eesel.
Frequently asked questions
What is an AI agent loop?
An AI agent loop repeats four jobs: take in the current context, choose a next step, use a tool or answer, then inspect the result. It stops when the task is complete, a limit is reached, or it needs a person. See our agentic AI guide.
How is an agent loop different from a chatbot?
A simple chatbot can answer one prompt. An agent loop can use the result of one lookup or action to decide the next one. Tool access alone does not make a system safe: it still needs limits and approvals.
What are the steps in an AI agent loop?
The useful shorthand is observe, decide, act, and verify. Names vary, but the important part is that a tool result changes what the agent does next.
Why does an AI agent loop need a stop condition?
Without a turn limit, cost limit, no-progress check, or handoff rule, a failed tool call can be repeated. A stopping rule turns an uncertain case into a reviewable handoff.
Can an agent loop resolve support tickets?
Yes, when it can retrieve approved context, follow a bounded procedure, and verify the result. For customer-facing work, connect it to AI helpdesk agent controls rather than treating a reply as proof of completion.
Article by
Alicia Kirana Utomo
Kira is a writer at eesel AI with a Computer Science background and over a year of hands-on experience evaluating AI-powered customer service tools. She focuses on breaking down how helpdesk platforms and AI agents actually work so that support teams can make better buying decisions.
