AI Agents Examples: What Four Real Ones Actually Do
Real AI agents examples — a coding agent, a support agent, an audit agent, a research agent — what each one's tool-call loop actually does, and where it fails.
Most lists of AI agents examples are really lists of product names, which tells you almost nothing about what the thing does. The useful version of the question is narrower: what tool does this agent call, what does it do with the result, and what tells it the task is finished? Every real agent is a language model wrapped in that same loop — AI agent vs LLM sets out the loop itself in full — and the differences between examples are just which tools sit inside it.
Four real loops
- A coding agent — write a function, run the test suite, read the failure, edit the file, run it again. The tool is a code sandbox; the stopping rule is the tests passing. Anthropic's own tool-use documentation describes exactly this propose-call-read-decide cycle, and products like Claude Cowork build a desktop agent around it.
- A support-triage agent — read a ticket, call a lookup tool against the account system, draft a reply using what came back. The tool is a database query; the stopping rule is a drafted reply ready for a human to send or approve.
- An audit agent — read a batch of transactions, flag ones that match a risk pattern, pull supporting documents for a human reviewer. EY's agentic AI deployment runs this pattern across more than 160,000 audit engagements, and KPMG's Clara platform does the same for expense vouching and unrecorded-liability checks for more than 95,000 auditors.
- A research agent — take a question, run several searches, read the pages that come back, and write a summary with what it found. The tool is a search function; the stopping rule is usually a turn limit rather than a real confidence check, which is the weakest stopping rule of the four.
Agentic workflows covers what actually runs unattended once a loop like this is set up, and agentic AI vs generative AI is worth reading if the product pitch in front of you does not say which of these four shapes it actually is.
A worked example: the coding agent loop
Weak task: "Fix the bug in this file."
Better task: "The test `test_discount_applies_once` is failing with AssertionError: expected 10, got 20. Read the test, read `pricing.py`, find why the discount is applied twice, fix it, and re-run just that test until it passes."
The better version gives the agent an actual stopping rule — the named test passing — rather than a vague goal it has to decide for itself when to stop chasing. Without that, an agent will often declare the job done after a plausible-looking edit, whether or not the test was ever re-run. This is the same gap how to write a prompt that works on the first try covers for a single request; an agent just gets to make that mistake multiple times in a row before anyone looks.
Where this goes wrong
The failure mode specific to agents, rather than a single chat reply, is a wrong tool call that still returns something — a lookup against the wrong account ID, a search that returns an unrelated page — and the agent treating that result as good, because nothing in the loop checks whether the tool call itself was aimed correctly. A survey of hallucination in large language models describes the underlying property: fluent output is not evidence the content behind it is right, and a loop that calls tools and reads results fluently is just as capable of that as a single answer is. More steps means more places for a plausible-but-wrong result to slip through unchallenged, not fewer.
Research agents show this most visibly, because a summary written from the wrong three search results still reads as a clean, confident answer. What AI is actually bad at covers the general version of this gap — fluency is not the same signal as accuracy — and it applies with no discount to a loop just because it searched before it wrote.
The checks, before you trust what it did
- For a coding agent, read the diff and re-run the test yourself — do not trust a transcript that says the test passed; run it.
- For a support or research agent, spot-check one tool-call result against the source it claims to have used — a wrong account ID or a misread page is the single most common failure, and it is invisible from the final answer alone.
- Give every agent an explicit, checkable stopping rule — a specific test, a specific document, a specific field — rather than a general goal it has to decide for itself when it has met.
- Set a turn or step limit and actually look at what happened if it hits that limit, rather than assuming it means the task was simply hard.
- Apply the same habit the NIST AI Risk Management Framework recommends for any AI deployment: decide what you will verify before the agent runs, not after something goes out wrong.
What to do Monday
Pick one of the four loops above that matches a task you already do by hand, write down its tool and its stopping rule explicitly, and test it against three cases where you already know the right answer before trusting it on a live one. AI agent builder covers what you are actually assembling if you are building this yourself rather than using a packaged product, and AI agents for SEO content creation walks through the same loop applied to a specific content task end to end.
Knowing which of these four shapes a product actually is, before trusting its output, is exactly the kind of judgement the market is paying more for: AI-skilled job postings grew 69% against 9% for the wider market, and the wage premium for those skills reached 62% according to PwC's 2026 analysis. Coursium teaches that judgement directly, through short lessons built around exactly this kind of worked example. Stay ahead of AI by learning the tools on your phone.