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AI agent examples: 14 real workplace use cases

By Faisal SaeedUpdated 5 min read

In short

AI agents are language models that read company data and use tools to finish a task, such as answering an HR policy question from the handbook, drafting a customer reply from order history, or preparing a sales brief from the CRM. Good workplace examples have a clear job, read only the data that job needs, and ask a person before they send, change or delete anything.

An AI agent is a language model that can use tools. Instead of only answering in text, it decides which steps to take, reads the data it needs and acts: it searches a drive, updates a ticket or drafts an e-mail. Anthropic’s guide Building effective agents describes agents as systems where the model directs its own process and tool use, unlike fixed workflows.

Below are 14 concrete examples grouped by team. For each, we list what the agent reads, what it does and what should need a person’s approval. The approval column matters: an agent that can do more than its job needs is one of the main risks in the OWASP Top 10 for LLM Applications (it calls this “excessive agency”).

What do good AI agent use cases have in common?

Before the list, a quick test. A good first agent:

  • Has one clear job that someone does often today.
  • Reads from a source people already trust, such as a handbook, CRM or ticket system.
  • Produces something easy to check, like an answer with citations or a draft.
  • Keeps risky actions behind approval, so mistakes are caught before they matter.

What are examples of AI agents for HR?

  1. HR policy assistant. Reads the employee handbook and benefits documents. Answers questions such as “how many days of parental leave do I get?” with a link to the policy. Needs approval: nothing, because it only reads. It should only see documents the asker is allowed to open.
  2. Onboarding coordinator. Reads the new-hire checklist, calendar and team pages. Creates a first-week schedule, books intro meetings and sends a welcome message. Needs approval: sending calendar invites and messages to other people.
  3. Candidate screening helper. Reads job descriptions and uploaded CVs. Summarises each candidate against the must-have criteria and asks the recruiter structured follow-up questions. Needs approval: any message to a candidate. Hiring decisions stay with people, and personal data should be masked where it is not needed.

What are examples of AI agents for finance?

  1. Spend analysis agent. Reads exported spreadsheets from the finance system. Totals spend by vendor or department, builds a pivot and a chart, and answers follow-up questions. Needs approval: none for analysis on a read-only copy.
  2. Invoice query agent. Reads invoices, purchase orders and the vendor e-mail inbox. Matches an invoice to its order and drafts a reply to the vendor. Needs approval: sending the reply, and any change to payment status.
  3. Month-end close assistant. Reads the close checklist and account balances. Tracks which steps are done, flags gaps and drafts the summary report. Needs approval: posting entries or changing records, which should stay with the accountant.

What are examples of AI agents for customer support?

  1. Support answer agent. Reads the help center and product documents. Answers customer questions on the website or in WhatsApp with sources. Needs approval: nothing for answers, but it should only reach public knowledge, never internal files.
  2. Ticket triage agent. Reads new tickets and past resolved tickets. Tags each ticket, sets priority, suggests the right team and drafts a first reply. Needs approval: sending replies to customers, at least until the team trusts it.
  3. Refund and order agent. Reads order history through an API. Checks eligibility and prepares a refund. Needs approval: always for refunds, or only above a set amount.

What are examples of AI agents for sales?

  1. Account brief agent. Reads CRM notes, recent e-mails and meeting notes. Writes a one-page brief before a customer call. Needs approval: none, because it produces a private draft.
  2. Proposal drafting agent. Reads past proposals, pricing sheets and product documents. Produces a Word or PowerPoint draft for the rep to edit. Needs approval: nothing to draft; sending it to a customer stays manual.
  3. Follow-up agent. Reads call notes and the CRM. Drafts follow-up e-mails and updates the next step in the CRM. Needs approval: sending e-mails and writing to the CRM.

What are examples of AI agents for IT and operations?

  1. IT help desk agent. Reads IT help pages and the ticket system. Answers “how do I” questions in Slack and opens a ticket when it cannot help. Needs approval: resetting access or changing accounts should always ask.
  2. Engineering status agent. Reads Jira or Azure DevOps boards and Confluence pages. Summarises what shipped, what is blocked and who owns it, on a schedule. Needs approval: none for summaries; closing or reassigning issues should ask.

How do these examples compare on risk?

Example Reads Acts on Suggested approval
HR policy assistant Handbook Nothing (answers only) None
Spend analysis agent Spreadsheets Nothing (analysis only) None
Account brief agent CRM, e-mail Private draft None
Ticket triage agent Tickets Tags, drafts Before customer replies
Onboarding coordinator Calendar, checklists Invites, messages Before sending
Follow-up agent CRM, notes E-mails, CRM fields Before sending or writing
Refund and order agent Orders Refunds Always, or above an amount
IT help desk agent Help pages, tickets Accounts, access Always for access changes

The pattern is simple: reads run freely, writes ask, and anything involving money, access or deletion always asks. See human in the loop AI for how to set this up.

How do you pick your first AI agent?

  1. List repeated questions your team answers every week.
  2. Pick one with a clean source: if the handbook is out of date, the agent will be too.
  3. Start read-only. Add write tools only once people trust the answers.
  4. Set approvals for every action that leaves the team or cannot be undone.
  5. Measure with real users: which answers were wrong, what was slow, what was missing.

As you add agents, you will need rules about who builds them and what they can reach. That is AI agent governance.

Further reading

How promptev handles AI agent use cases

  • You build an agent from plain-word instructions, knowledge, tools and a model on your own keys, test the draft in a built-in chat and publish a version. Ori, the builder assistant in the console, can help build one from a request like “build an agent that answers HR questions from our handbook”.
  • Knowledge can be uploaded or connected from sources such as Google Drive, SharePoint, OneDrive and Dropbox, and answers cite their sources and follow the asker’s permissions for those sources.
  • Connectors cover Google Workspace, Microsoft 365, Dropbox, Confluence, Jira, Azure DevOps and Slack, plus any HTTP API, remote MCP server or database (read-only by default, limited to picked tables).
  • Agents can generate Word, PDF, Excel, PowerPoint and other files, run code over spreadsheets for sums and pivots, and ask people structured questions.
  • Each tool has an approval setting (reads none, writes ask once and destructive actions always ask by default), and builders can change it.
  • Agents reach people in the Agent library, Slack, WhatsApp, a website widget, an HTTP API and MCP. See the App.

Frequently asked questions

What is an example of an AI agent?

A customer support agent that reads the help center and a customer's order history, drafts a reply, and asks a person to approve before issuing a refund. It decides which steps to take and uses tools to take them.

What is the difference between an AI agent and a chatbot?

A chatbot answers in text. An AI agent can also use tools: it searches systems, updates records, creates files or sends messages, deciding the steps itself.

What is the best first AI agent for a company?

A read-only question-answering agent over a well-kept source, such as an HR handbook or IT help pages. It is useful from day one, easy to check and low risk because it does not change anything.

Which AI agent use cases need human approval?

Anything that sends messages outside the company, moves money, changes access or deletes data. Reads and internal drafts usually do not need approval.

Can AI agents replace employees?

In most workplaces agents take over repetitive steps such as searching, summarising and drafting, while people keep the decisions and the exceptions. Agents that act without review on high-stakes work are still rare and risky.