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What counts as an AI agent?
OpenAI’s practical guide to building agents describes agents as systems that independently accomplish tasks on a user’s behalf. They use a language model to manage workflow execution and make decisions, choose tools to gather context or take action, recognize when work is complete, and stop or return control when necessary.
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This makes the distinction operational. A chatbot that answers a question in one turn, or a classifier that labels a message, is not necessarily an agent. An agent carries a task forward across steps—for example, retrieving an order record, applying a return policy, and preparing a refund request. Generative AI can be part of an agent, but generation alone does not make a feature an agent.
AI agent examples in customer support
Each example below is a workflow pattern documented by OpenAI or Google Cloud, not evidence of independently measured outcomes. What an agent can actually do depends on the organization’s data, integrations, permissions, and review rules.
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1. Technical troubleshooting
A support agent can ask what the customer is experiencing, search a knowledge base, and guide the customer through relevant diagnostic steps. For an outage or product issue, it can collect details and use approved tools to retrieve context. A useful completion condition might be a confirmed resolution or a support handoff that includes the symptoms and steps already tried. OpenAI describes technical-support agents that answer product questions, help resolve issues, and search knowledge bases.
2. Order tracking and delivery questions
An order-support agent can identify the relevant order, retrieve tracking or delivery-schedule information, and explain the status. It needs access to the appropriate order and shipping records, with identity checks where the business requires them. The task should end with a clear answer or a handoff when the record is unavailable or the customer’s request falls outside the agent’s permissions.
3. Returns, refunds, and replacements
For a return request, an agent can gather the order details and reason, check applicable policy, and prepare or initiate the next permitted step. Google Cloud documents a damaged-item scenario in which a customer reports a broken or defective product and asks for a replacement or refund. The workflow can branch according to the information collected and include manual steps or tool calls; consequential actions can wait for human approval.
That approval boundary matters. An agent might collect evidence and recommend a remedy without being authorized to issue a refund or ship a replacement. The workflow should make explicit which actions are automatic, which need approval, and what happens when required information is missing.
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4. Sales assistance and purchase support
A sales-support agent can help an enterprise customer browse a product catalog, compare suitable solutions, and facilitate a purchase. OpenAI’s example includes a purchase-order action. In practice, placing or preparing an order requires an appropriate integration and permission; it is not a capability that follows automatically from adding a chat interface. A sensible flow distinguishes product guidance from a transaction and gives the customer or an authorized employee a chance to confirm the purchase.
5. Appointment inquiry, cancellation, or scheduling
A scheduling agent can validate a customer, retrieve an existing appointment, and confirm its details. A broader workflow can handle inquiry, cancellation, or scheduling through different branches. Google Cloud’s examples show how a sequence can combine information gathering, system actions, and confirmation. The success condition should be a verified appointment state—not simply a message saying the request was received.
6. Support escalation summaries
An event-triggered agent can turn a support escalation into a concise handoff containing the issue, relevant context, actions already attempted, and the next decision needed. OpenAI’s API-trigger examples include support escalation summaries. The summary is useful only if it reaches the intended destination and preserves the information a human needs; a draft can be routed for review rather than sent automatically.
AI agent examples beyond customer support
Briefings from multiple sources
A briefing agent can gather information from several approved sources, compare signals, and produce a memo for a defined audience. The task needs a clear scope—what sources count, what the audience needs, and what format the output should take. A person may need to review the memo, particularly when it informs a consequential decision.
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Sales meeting preparation
An OpenAI workspace-agent example finds upcoming customer meetings, excludes internal-only meetings, gathers account material, searches for recent company news, and creates a meeting brief. This is more than summarization: the agent coordinates several retrieval steps and produces a work product for a specific meeting. Its usefulness depends on access to the relevant calendar and account materials and on rules that prevent internal meetings from being treated as customer appointments.
Employee helpdesk triage
An employee-support agent can respond to a helpdesk event by classifying the request, collecting necessary context, and preparing a route or summary for the right team. OpenAI’s API-trigger cookbook lists employee helpdesk triage as an event-triggered use case. Triage can support a human queue without granting the agent authority to resolve every request itself.
Recurring reports and team updates
A reporting agent can respond to a scheduled or system event by summarizing new records or preparing a team update. OpenAI’s API-trigger examples include weekly reporting. The workflow should specify the reporting period, source records, output destination, and owner, so recipients can tell what the report covers and who is responsible for it.
Repeatable cross-team work
OpenAI Academy’s workspace agents material frames agents as a way to handle recurring work across shared systems, standard handoffs, and consistent outputs. Its governance examples include keeping recommendations in draft, escalating high-priority issues, and requiring approval before submission or budget changes. These are design patterns, not a guarantee that a particular integration or automated action is available in every organization.
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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 errorsConversational agents versus structured workflows
A conversational agent and a structured workflow solve different parts of a task, and they can be combined. Google Cloud’s chat agent overview describes conversational agents as a fit for open-ended exchanges, dynamic tasks that depend on user input, and questions or personalized data lookup. Its workflow documentation describes sequences of steps that may combine AI with human intervention.
| Decision point | Conversational agent | Structured workflow |
|---|---|---|
| How fixed is the path? | Useful when the next question depends on what the user says. | Useful when required steps or known branches need to be tracked. |
| Who carries out the action? | Can gather details or look up personalized information through approved tools. | Can guide a person, collect information, call an approved tool, or combine these roles. |
| How is oversight applied? | Set limits on tool use and define when the agent must hand the conversation to a person. | Attach approval or human tasks to specific steps and branches. |
| What proves completion? | A resolved question or a valid handoff, as defined for the task. | A verifiable end state, such as a confirmed appointment or delivered escalation summary. |
For example, conversation can uncover what went wrong with an order while a structured flow ensures identity checks, policy steps, and approvals are not skipped. The conversational layer adapts to the customer’s description; the workflow preserves mandatory controls.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to scope a first agent workflow
Start with work that recurs, has a clear success condition, and benefits from an agent’s flexibility. OpenAI’s guidance emphasizes well-defined success criteria, suitable workflows, bounded tool access, and the ability to halt or transfer control. Its API-trigger guidance recommends beginning with one narrow workflow, one clear source event, and one output destination.
- Choose one repeated task and its trigger. Define the customer request or system event that starts the work, such as a damaged-item claim or a new escalation.
- Specify the finished state. State what must be true when the agent is done: for example, an appointment has been confirmed or a support summary has reached the correct queue.
- Limit sources and tools. Identify the knowledge, records, and actions required for this task; do not grant broad access merely because it is available.
- Make the procedure explicit. Turn existing support scripts, policies, or operating instructions into steps the agent can follow. OpenAI’s guide recommends grounding customer-service routines in existing operating materials.
- Define exceptions and approvals. Decide what happens when data is missing, policy does not cover a case, an action needs approval, or the task should go to a person.
- Test representative cases before expanding access. Include ordinary requests and exceptions, check whether the output meets the success condition, and confirm that the agent stops or escalates when it should. Add context or destinations only after the narrow workflow behaves consistently.
OpenAI’s API-trigger cookbook gives the narrow-start principle in practical terms: one workflow, one source event, and one output destination before adding more context or destinations.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchWhat a complete agent example should specify
A useful example is not just a task label such as “handle returns.” It describes the conditions that let a team assess whether the workflow is feasible and safe:
- Trigger: the customer request, scheduled event, or system change that starts the work.
- Inputs: the records or customer details needed to proceed.
- Tools and permissions: what the agent may look up, change, or submit, and what remains unavailable.
- Steps and branches: the required checks, possible paths, and how incomplete or exceptional cases are handled.
- Success condition: a verifiable result, not merely a plausible-sounding response.
- Human controls: where approval is needed and when the agent must hand the task to a person.
- Output owner: the person or system that receives the result and is accountable for the next step.
These details distinguish a credible workflow design from a broad promise of automation. The examples describe intended patterns and capabilities; they do not establish quantified performance or guarantee that a particular deployment will succeed.
Frequently Asked Questions
What is the difference between an AI agent and a chatbot?
A chatbot may answer or classify without managing a task. An agent uses context and tools to carry work through steps, determine whether it is complete, and stop or hand control to a person when needed.
Can an AI agent issue refunds or place orders?
Only if it has an appropriate integration and explicit permission. A workflow can instead collect details or prepare an action for human approval.
Should a support team use a conversational agent or a structured workflow?
Use conversation when the next question depends on the customer’s input; use a structured workflow when required steps, branches, or approvals must be tracked. They can be combined.
What is a good first AI agent use case?
A narrow, repeated task with a clear trigger, available information, bounded tool access, and a verifiable completion condition is a suitable starting point.
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