AI Agents Examples for Business

Eight examples, by department

Each example below has a narrow job. The columns show what matters when you evaluate one: what the agent decides, what it may touch, and where a person checks. Prices come from vendors' own pages, checked in November 2026. We did not test any product.

What the examples have in common

Every working example in the table has the same shape. A narrow job, a limited set of things it can touch, a written list of things it must refuse and a person who checks the output. The ones that fail in practice usually break one of those four.

Anthropic's engineering guidance draws the same line between workflows and agents: in a workflow, models and tools follow code paths you wrote, while in an agent the model directs its own process. It recommends the simplest solution that works. Several rows above, such as finance and operations, are really workflows with one AI step, and calling them agents adds nothing but risk.

Which examples look good in a demo and fail in real life?

Be cautious of examples that remove the person completely. Three patterns come up often:

• The autonomous outbound salesperson. It finds prospects and emails them with no review. Consent rules for email and SMS apply, and platforms such as LinkedIn do not allow third-party tools that automate activity on members' accounts, so this can end in blocked accounts and complaints.
• The agent that manages your inbox. It reads, replies and archives on your behalf. One wrong send to a customer cannot be recalled, so keep it to drafts.
• The universal researcher. It browses, reads and writes up anything. It also invents sources. Use it for a first pass and check every fact you will repeat.

How should you read an example?

Ask five questions of any example, whether it is a product page or a code repository:

1. What triggers it? An event you can name.
2. What does it decide on its own? Ask for a concrete sentence, not a slogan.
3. What can it touch? List every system and the level of access.
4. What does it refuse, and how do you know? Ask to see a refusal.
5. Where is the log? You should be able to read what it did yesterday.

Pricing units mislead, so compare cost per resolved conversation

The examples price in different units, so the headline numbers cannot be compared directly. This is illustrative arithmetic.

Meta's developer documentation puts its Business Agent at USD 2.00 per million tokens, roughly 4 to 5 cents a message. If an average conversation takes four AI replies, that is about 16 to 20 cents a conversation. Intercom's Fin is USD 0.99 per outcome, where an outcome is a confirmed resolution, no further request for help, or a completed workflow. Salesforce lists USD 2 per conversation.

On paper that makes the per-message option look five times cheaper than Fin. It might be, or it might not. A cheap reply that fails to resolve the question costs you a conversation and a customer. The only number that decides it is cost per resolved conversation, and you can only measure that by running each option on a sample of your own conversations for a month.

Should you buy, build or hire?

Whatever you choose, sign a data processing agreement with any vendor that handles customer data, as GDPR Article 28 requires of processors. For the detail on one common example, see AI customer service agents.

Where to start

Pick the example closest to your biggest time sink, run it on a small sample with a person checking, and measure cost per resolved conversation or minutes saved. Our guide to AI agents for business explains the agent versus workflow test, and AI agents for small business puts hours and euros on five of these. For help choosing and building, ask about our AI automation service. We are a new studio, and we quote in writing.