Here are 25 AI agent examples that commercially available tools do today, grouped by department, with the risk level of each. The pattern across all of them is the same: the task repeats, the rules are mostly clear, and the agent's work is cheap to check. Finance and admin jobs dominate the low-risk end; recruitment and anything touching money or people's rights sit at the high end and need a human decision-maker. Use the table to find your first agent and the notes to avoid the common first mistake.
Key facts
- 25 examples across finance, customer service, sales, admin, marketing, recruitment, HR, operations and engineering.
- Best first agent: invoice chasing. Most mature: coding. Highest risk: CV screening and anything deciding about people.
- Common thread: repetitive, rule-bound, cheap to check.
- UK SMEs: 54% now use AI (BCC 2026); 95% report no headcount impact.
On this page
- Which jobs do people hand to an AI agent first?
- AI agents: 25 examples by department
- What do the good examples have in common?
- Which examples should you avoid as a first agent?
- Frequently asked questions
- Sources
Which jobs do people hand to an AI agent first?
AI agents: 25 examples by department
| # | Agent | What it does | Department | Risk |
|---|---|---|---|---|
| 1 | Invoice chaser | Finds overdue invoices, sends reminders in your tone, checks payment, escalates | Finance | Low |
| 2 | Bank reconciliation | Matches transactions to invoices and bills, queries exceptions | Finance | Low |
| 3 | Expense coder | Reads receipts, codes them to the ledger, flags policy breaches | Finance | Low |
| 4 | Support triage | Reads each ticket, answers routine ones, routes the rest with a summary | Customer service | Medium |
| 5 | Refund handler | Checks eligibility against policy, issues refunds within a limit, logs | Customer service | Medium: cap the limit |
| 6 | Review responder | Drafts replies to online reviews for approval | Customer service | Low |
| 7 | Lead qualifier | Looks up the company, scores fit, drafts a tailored reply, offers times | Sales | Low |
| 8 | CRM hygiene | Finds stale deals and missing fields, drafts the update | Sales | Low |
| 9 | Proposal drafter | Assembles a first-draft proposal from past wins and the brief | Sales | Low |
| 10 | Inbox summariser | Morning digest: needs a reply, needs a decision, can wait | Admin | Low |
| 11 | Meeting prep | Finds a slot, sends invites, writes a one-page brief from past emails | Admin | Low |
| 12 | Notes to actions | Turns meeting notes into owners, dates and follow-ups | Admin | Low |
| 13 | Competitor monitor | Checks named sites weekly, summarises changes, flags price moves | Marketing | Low |
| 14 | Content repurposer | Turns one article into a newsletter, posts and a summary for approval | Marketing | Low |
| 15 | SEO monitor | Watches rankings and errors, drafts the fix list | Marketing | Low |
| 16 | CV screener | Scores applications against a written brief, drafts the shortlist | Recruitment | High: human decides; UK GDPR 22A-22D |
| 17 | Onboarding checklist | Issues accounts, schedules training, chases missing documents | HR | Medium |
| 18 | Policy Q&A | Answers staff questions from the handbook, escalates the rest | HR | Low |
| 19 | Stock reorder | Watches levels and lead times, drafts purchase orders | Operations | Medium: human approves spend |
| 20 | Supplier chaser | Tracks deliveries, chases late ones, updates the tracker | Operations | Low |
| 21 | Research brief | Searches dozens of sources, cross-checks, writes a cited summary | Everyone | Low |
| 22 | Document summariser | Summarises long PDFs and contracts, pulls out dates and duties | Everyone | Low |
| 23 | Coding agent | Writes, runs, tests and fixes code in a repository | Engineering | Low, inside version control |
| 24 | Site monitor | Checks uptime and errors, opens tickets, drafts the fix | Engineering | Low |
| 25 | Form filler | Completes repetitive web forms from your data | Everyone | Medium: fragile; human on payment |
What do the good examples have in common?
- They repeat. An agent earns its setup time on the hundredth run, not the first.
- The rules can be written down. If you could brief a temp in a page, you can brief an agent.
- The output is checkable. A draft, a list, a reconciled ledger. You can see whether it is right in seconds.
- A mistake is cheap. An awkward reminder, a mis-filed receipt. Not a lost customer or a legal claim.
- The tools are narrow. Read the ledger and send email; not "everything on the shared drive".
Anthropic's guidance for builders says the same from the other side: find the simplest solution possible and add complexity only when needed, because agents trade cost and speed for flexibility. Most of the 25 above are better built as workflows with AI steps than as fully autonomous agents, at least to begin with. The difference is explained in What is agentic AI?
Which examples should you avoid as a first agent?
- CV screening and anything deciding about people. UK GDPR Articles 22A-22D require safeguards and meaningful human involvement for solely automated significant decisions, and the ICO's 2026 recruitment report found employers routinely underestimating when a tool was deciding. Fine as a shortlist drafter with a human decision-maker; not as a first project.
- Payments and purchasing without a cap. Agents can be manipulated by content they read. Put a spend limit and an approval step on every one.
- Open-ended "run my business" agents. Long, multi-judgement tasks are where agents drift. Gartner expects more than 40% of agentic projects to be cancelled by the end of 2027; the vague ones go first.
- Anything that speaks for you to people who matter without approval. Build trust on low-stakes contacts first.
Frequently asked questions
What are some examples of AI agents?
Invoice-chasing agents, support-inbox triage agents, lead-qualification agents, meeting-prep agents, research agents, bank-reconciliation agents, CV-screening agents and coding agents such as Claude Code and Codex. This page lists 25 across eight departments.
What is the most successful use of AI agents so far?
Coding. Agents that write, run, test and fix code are in daily use at most software teams and proved the techniques now used elsewhere.
What is a good first AI agent for a small business?
Invoice reminders. The job repeats, the rules are clear, the agent's work is easy to check, and a mistake is a slightly awkward email rather than a lost customer.
Can AI agents replace employees?
In UK small firms the evidence so far says no: 95% of SMEs using AI report no impact on headcount, and most say it supports staff. Agents remove repetitive hours, not roles, at least today.
Are these examples real or hypothetical?
All 25 are jobs that commercially available tools do today. Where a job still needs a human on every step, the entry says so. For the newest tools, see the best new AI agents on Product Hunt, each rated for UK users.
Next: build the number one example with How to set up an invoice-chasing AI agent.
Read next
- The best new AI agents on Product Hunt, reviewed
- AI agent marketplaces: 20 stores reviewed with UK prices
- Types of AI agents: the five you can buy, and the five in the textbook
- AI for accountants and bookkeepers
- AI agent vs chatbot vs AI assistant: the difference
- OpenClaw explained: the free AI agent you run yourself
- Meta Muse: what it does and whether you can use it in the UK
Sources
- British Chambers of Commerce / Atos survey, reported by SIA (2026) - 54% adoption; 95% no headcount impact
- Anthropic, Building effective agents (December 2024) - simplest solution first
- Inside Privacy, ICO draft ADM guidance and recruitment report (April 2026) - recruitment findings
- RCR Wireless, Gartner forecast (June 2025) - 40% cancellation forecast
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