AI Agent Examples: 17 Named Deployments and 30 Tasks in 2026

The word "agent" gets stretched to cover almost anything with a chat box, so the fastest way to understand agents is to look at what they do. This guide starts with 17 named organisations running agents in production, each row labelled with who is making the outcome claim, then lists thirty everyday agent examples grouped by the job they replace. For the definition first, see our guides to what an AI agent is and what an AI agent can actually do.

What counts as an AI agent example?

An AI agent example is a single, bounded task where you hand over a goal and get back finished work. It is not a chatbot reply or a fixed script. You own the outcome, the agent owns the steps, and it checks its own results before handing them over.

That framing matters because the label does a lot of marketing work in 2026. Gartner put a name to the problem: agent washing, the rebranding of chatbots, robotic process automation and plain assistants as agentic AI. Of the thousands of vendors claiming agentic capability, Gartner estimated in June 2025 that only about 130 are the real thing (Gartner, 2025). So the useful test is behavioural, not semantic: if you still have to carry the output somewhere for it to count, you have an assistant. Our breakdown of AI agent vs chatbot vs assistant draws the line in detail, and the top AI agent use cases for H1 2026 covers the wider view.

AI agent examples at named companies

Seventeen organisations have published, or had reported, a production AI agent with enough detail to name the system and the job it does. The table below is that list, and the outcome column carries whatever number was actually published, including "none". Four of the seventeen have no outcome metric at all, which is worth knowing before anyone copies one of these into a business case.

The fourth column is the one the competing lists do not have. Every outcome figure here was produced by someone with an interest in it, so each row says who is making the claim. Company's own claim means the organisation said it about itself. Vendor marketing means the software supplier published it, in two cases about its own product on its own website. Independent reporting means a journalist or trade outlet published it, though in nearly every case the outlet is relaying the customer's figures rather than auditing them. None of that makes a number false. It does mean none of these numbers is an audit, and a list that prints them all in the same typeface is hiding a real difference from you.

One sourcing note before you reuse any of this. Capcom, Tata Steel and Home Depot all come from a single Google Cloud round-up published on 22 April 2026. That is one source, not three independent confirmations, and the same round-up names further companies, including Merck and Citi Wealth, with no outcome figures at all. Only Commonwealth Bank, Rolls-Royce, JPMorgan, Goldman Sachs and Klarna have two genuinely independent legs. For deployments written up with fuller financial detail, see our AI agent ROI case studies roundup.

What the outcome column leaves out

The gap between a headline number and a measured result is where most agent business cases go wrong. Six of the seventeen rows above need a caveat attached before the number means anything.

• Uber published no metric at all. The Finch write-up contains no time saved, no query volume, no adoption figure and no accuracy figure. The absence is the finding: a serious engineering team shipped a real agent and chose not to publish a productivity number.
• JPMorgan's 20% is not an agent result. It is year-over-year gross sales growth with no isolation of the software's contribution, and the "nearly $1.5 billion" figure often attached to Coach AI covers fraud prevention and operations broadly. Coach AI is also closer to a retrieval assistant than an autonomous agent.
• IBM's "containment" is undefined. A 94% containment rate is not a 94% resolution rate. Containment usually means the conversation did not escalate, which includes the people who gave up.
• Dow's savings have not happened yet. The write-up leans on an industry benchmark of about 3 per cent of freight expense rather than a realised result, and the proof of concept is now 22 months old with no follow-up.
• Universal Health Services went quiet. The June 2025 announcement promised expansion to more than 10 additional US hospitals "within the next few months". That was 15 months ago, and no follow-up confirming it could be found. Pilot announced, then silence, is the most common shape in agent case studies.
• Rolls-Royce's biggest number has no derivation. The 300,000 saved shop-floor hours come with no stated method, and every figure is the customer's own, delivered from a stage at the vendor's conference.

Salesforce sits at the bottom of the independence ranking structurally: the vendor is also the customer, the case study is on the vendor's own domain, "resolved" is never defined, and the page is nearly 22 months old. OpenAI's numbers come with an unusually honest caveat from the outlet that reported them, which called them "OpenAI's figures, measured against OpenAI's own grading criteria, on OpenAI's own channel" and added that "the transparency is welcome, but the numbers are not independently verified". That sentence belongs on most rows of most agent listicles, and almost never appears.

Five figures that other example lists get wrong

These five errors are actively circulating, and every one of them was produced by a listicle copying a round-up without re-reading the original sentence.

• The network operations numbers are Far EasTone's, not Vodafone's. Search results routinely attribute the 60% and 10,500-tasks figures to Vodafone. They belong to Far EasTone Telecom.
• Virgin Voyages' "up to 60%" is not about its concierge agent. In the source, it refers to production timelines under Google Distributed Cloud.
• Citadel Securities' "four times faster" is not an agent outcome. It describes TPU research workloads in the same round-up.
• Uber's Finch has no "hours to seconds" claim. See above: that phrasing was added by someone else.
• Goldman's "20% productivity" was never measured by Goldman. It originates as an analyst's conditional: if Devin increases productivity by 20%, then 12,000 technologists can operate like 14,400. The conditional got dropped somewhere in the retelling, and Goldman's deployment was a pilot.

Klarna: the world's most-quoted agent statistic described a one-month-old deployment

Klarna's "work of 700 full-time agents" is the single most cited AI agent number in existence, and almost every page repeating it omits two facts. The first: when Klarna published the claim on 27 February 2024, the assistant had been live globally for one month, and no methodology was stated for the 700-agent equivalence or the estimated $40 million profit improvement. The release was co-published with OpenAI, so it is a company claim and vendor marketing at the same time.

The second fact is the reversal. By May 2025 Klarna was rehiring human agents. CEO Sebastian Siemiatkowski said the quiet part out loud: "From a brand perspective, a company perspective... I just think it's so critical that you are clear to your customer that there will be always a human if you want." He told Bloomberg that cost had been "a too predominant evaluation factor", producing "lower quality". Notably, the assistant did not fail; at the point of reversal it still handled two-thirds of all customer inquiries. What failed was the decision to let cost drive the staffing model.

The honest version, then: a genuinely large deployment, an over-confident number published a month in, and a public correction of the strategy 15 months later. Competing lists still cite the February 2024 figure flat, 31 months on. For the pattern rather than the anecdote, see our write-up of AI agent failures and what they taught us.

Commonwealth Bank: deploy, over-claim, reverse, apologise, then succeed

Commonwealth Bank of Australia is the only case here with the full arc, and it is the most useful example on the page for that reason. In August 2025 the bank cut 45 onshore customer service roles, citing an AI voice bot. Call volumes then rose rather than fell. The bank reversed the decision, called it an "error" and apologised. That is independent reporting from ABC News, not a vendor case study, and no competing example list tells this part.

The part most people miss is what happened next. By May 2026, across more than two million customer conversations a month, the same deployment was resolving the overwhelming majority of them without a human. Both things are true: the staffing decision was wrong and premature, and the technology worked about a year later. Anyone using CommBank as evidence for either "AI agents replace support teams" or "AI agents do not work" is quoting one chapter of a four-chapter story.

The reporting also contains a clean lesson in reading agent metrics. The same May 2026 period appears both as "almost nine in every 10 customer conversations" resolved without a human and as an "84.6% self-service resolution rate". Those are not contradictory, they are different denominators: one counts all conversations, the other counts messaging interactions. One deployment, one month, two headline numbers, and a listicle picks whichever suits its argument. Our guide to an AI customer service agent works through what each denominator measures.

How many of these projects survive

Most agentic AI projects will not reach production, and the best available number says so plainly: Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027, driven by escalating costs, unclear business value or inadequate risk controls. That figure deserves its own caveat, in keeping with the rest of this page. It rests on a January 2025 poll of 3,412 webinar attendees, which is a self-selected sample of people who chose to attend a webinar about AI, not a random sample of enterprises. Read it as a direction of travel from a Tier 2 analyst, not as a measured cancellation rate.

The same Gartner release explains why the example lists look so crowded. Out of thousands of vendors describing themselves as agentic, Gartner estimated only about 130 genuinely are. Most of the rest are existing chatbots, robotic process automation or assistants with a new label, a practice Gartner calls agent washing. That is the practical reason this guide separates named production deployments from the everyday task examples below: the first group is small and the second group is where the honest, checkable wins are.

None of this argues against running an agent. It argues for choosing a first example small enough that cancellation costs you a week rather than a quarter, which is what the next six sections are for. For a longer look at where the enthusiasm outruns the evidence, see are AI agents overhyped and our read of the Gartner hype cycle for AI agents.

AI agent examples for inbox and email

Email is the most reliable home for agents: high volume, repetitive, and easy to check. These agents read, sort, draft, and route messages against rules they learn from you, then leave the results for you to approve. You skim the exceptions instead of the whole pile.

• Inbox triage agent. Classifies new mail, drafts routine replies, flags the urgent, and archives the rest.
• Meeting follow-up agent. Writes the recap from the call and sends agreed next steps to attendees.
• Attachment filing agent. Pulls invoices and receipts out of email and files them with consistent names.
• Routine auto-responder. Answers the questions you get a dozen times a day and hands off anything unclear.
• Label and routing agent. Tags incoming mail by topic and forwards each thread to the right queue.

AI agent examples for sales and leads

Sales work rewards consistency more than brilliance, which is exactly what an agent gives you. These examples keep the pipeline moving while you focus on live conversations: no follow-up forgotten, no record left stale, every lead touched on time instead of getting a generic blast.

• Cold lead follow-up agent. Sends spaced, personalised nudges to quiet leads and stops when they reply.
• Lead qualification agent. Scores new inbound against your ideal-customer profile and routes strong fits onward.
• CRM hygiene agent. Fills missing fields, merges duplicates, and corrects stale deal stages.
• Deal-stage nudge agent. Spots deals stuck too long and prompts the owner with the next action.
• Meeting-to-CRM logger. Turns a sales call into a clean CRM note with next steps and a date.

AI agent examples for reporting and analytics

Reporting is where hours vanish into copy, paste, and reformatting. Agents are strong here because the sources are structured and the output has a fixed shape. You describe the report once, and a finished version lands in your inbox on schedule without you assembling it.

• Financial report agent. Pulls numbers from your accounting tools into a clean monthly or weekly report.
• Analytics summary agent. Reads your analytics and writes a plain-English note on what moved and why.
• Competitor tracking agent. Watches rivals' pricing and pages, then surfaces only the changes worth your time.
• KPI anomaly watcher. Alerts you the moment a metric moves outside its normal range.
• Board-prep agent. Gathers recurring numbers into the same deck structure every cycle.

AI agent examples for operations and admin

Back-office work is full of small, deadline-bound tasks that are easy to drop when you are busy. These agents keep the routine running: money chased, expenses coded, calendars tidy, and messy data cleaned, all without you setting a reminder for every step.

• Invoice chasing agent. Tracks overdue invoices, sends escalating reminders, and flags accounts needing a call.
• Expense categorisation agent. Reads receipts and card feeds, codes each spend, and flags oddities.
• Calendar scheduling agent. Finds mutual free slots, proposes times, and books without the back-and-forth.
• Data cleanup agent. Standardises formatting, removes duplicates, and fixes broken fields.
• Onboarding checklist agent. Runs a new hire or customer through every setup step and nudges blockers.

AI agent examples for marketing and content

Marketing agents shine at the repetitive middle of the work: turning one asset into many, watching channels, and keeping the posting pipeline moving. They do not replace the strategy, they remove the manual reformatting and monitoring that eats a marketer's week.

• LinkedIn content agent. Drafts posts in your voice from a rough idea and queues them for review.
• Content repurposing agent. Turns one long piece into a thread, a newsletter blurb, and short posts.
• SEO monitoring agent. Watches rankings and indexing for your key pages and reports drops early.
• Social listening agent. Tracks brand mentions and summarises sentiment and anything worth a reply.
• Content calendar agent. Keeps the schedule current, flags gaps, and reminds owners of due drafts.

AI agent examples for personal tasks

Agents are not only for work. The same pattern, one goal handled end to end, applies to the errands that clutter a personal to-do list. These examples handle recurring household admin so it stops living in your head and runs on a schedule you set.

• Grocery reorder agent. Rebuilds your usual cart on a schedule and flags out-of-stock items.
• Subscription audit agent. Surfaces recurring charges you forgot and says what is safe to cancel.
• Trip planning agent. Assembles a shortlist against your dates and budget so you just choose.
• Appointment booking agent. Finds a slot, books it, and adds it to your calendar.
• Bill reminder agent. Watches due dates and nudges you before anything lapses.

Which AI agent example fits your work?

The right first example depends on which task costs the most time and is easiest to check. The table below lines up one agent per job area against the outcome it hands back and how often it runs, so you can match one to a real gap in your week.

How to try an AI agent example

You have three routes to any example above, in increasing order of ease: build one with developer frameworks, assemble one in a no-code workflow tool, or run an expert-built one on a managed platform. The first two are real software projects with upkeep attached to them.

• Build it yourself. Full control and full maintenance: code, testing, and upkeep every time a connected tool changes.
• Assemble it no-code. Faster for fixed processes. Our guide to the best no-code AI agent platforms covers where this stalls.
• Run an expert-built agent. Describe the task in plain words and the right agent deploys in about 60 seconds, with limits you control.

That third path is what Gravity, the AI agent platform, is built for. Your first agent is free and needs no card, so you can try an example at no cost. Autopilot is 20 dollars a month worldwide, or 1,999 rupees a month in India, and Minipilot is 399 rupees a month in India. For a wider comparison, see the cheapest AI agent platforms breakdown, and our AI agent pilot program guide for scoping a first deployment that survives the Gartner base rate above. Gravity is in private alpha; when it opens, we email you: apply to the alpha.

Frequently asked questions

What is an example of an AI agent?

A common example is an inbox triage agent. You give it one goal, keep my inbox under control, and it reads new mail, classifies each message, drafts replies to routine ones, flags anything urgent, and archives the noise. You review the finished work rather than doing the sorting yourself.

What are the most common AI agent examples at work?

The most common workplace AI agent examples are bounded, repeatable tasks: inbox triage, lead follow-up, CRM hygiene, weekly report drafting, invoice chasing, expense categorisation, and analytics summaries. They share a pattern of clear inputs, a checkable output, and tolerance for the odd handoff to a human.

Are AI agents and chatbots the same thing?

No. A chatbot answers one message at a time and stops, so you still have to act on what it says. An AI agent holds a goal across many steps, takes real actions through tools like email and spreadsheets, and hands back finished work instead of just words.

Can I get an AI agent example without coding?

Yes. On a platform that runs expert-built agents, you describe the task in plain words and the right agent deploys in about 60 seconds. Coding only enters the picture if you decide to build your own agent with developer frameworks, which is a separate path meant for engineers.

How much do these AI agent examples cost to run?

On Gravity, pricing is a straightforward subscription. Your first agent is free and needs no card, so you can try an example at no cost. Autopilot is 20 dollars a month worldwide, or 1,999 rupees a month in India, and Minipilot is 399 rupees a month in India.

What is a good first AI agent example to try?

Start with a task you could explain to a new hire in one paragraph and check in under a minute. Inbox triage, weekly report drafting, and cold lead follow-up are strong first examples because the input is clear, the output is easy to verify, and a mistake is low stakes.

What are examples of AI agents?

AI agent examples fall into two useful groups. The first is production deployments at named organisations: Rolls-Royce runs IT service desk agents, Capcom runs game playtesting agents, Far EasTone Telecom runs network operations agents, and Uber runs a Slack agent called Finch that queries financial data marts in plain language. The second group is the everyday task agents most people can actually run themselves: inbox triage, cold lead follow-up, weekly report drafting, invoice chasing, content repurposing, and grocery reordering. Most readers should start with the second group, because the task is small enough to check in a minute.

What is an example of an AI agent in real life?

Capcom's game playtesting agents are one of the most concrete real-life examples on record. Google Cloud says they autonomously navigate massive digital worlds to identify bugs, visual glitches and audio inconsistencies, logging more than 30,000 hours of testing per month. Treat the number carefully: it comes from a vendor round-up published in April 2026, not from an independent audit. A smaller real-life example you can verify for yourself is an inbox triage agent that sorts your mail each morning, drafts the routine replies, and leaves the exceptions for you.

What is the difference between an AI agent and an AI assistant?

An AI assistant waits for you and answers. An AI agent holds a goal across many steps, acts on it through your tools, and hands back finished work. The practical test is who does the next step. If you still have to copy the answer somewhere for it to count, you used an assistant. If the work is already done and you are reviewing it, you used an agent. Plenty of products marketed as agents are assistants, which is why Gartner gave the practice a name: agent washing.

Sources

• Gartner, "Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027", 25 June 2025, gartner.com. Source of the 40% cancellation prediction, the agent-washing estimate of about 130 genuine vendors, and the January 2025 poll of 3,412 webinar attendees.
• diginomica, "ServiceNow Knowledge 2026", 5 May 2026, diginomica.com. Rolls-Royce and ServiceNow figures, quoted from named speakers at the vendor's own conference.
• ACS Information Age, "AI drives fresh CommBank job cuts", 30 July 2026, ia.acs.org.au. CommBank resolution and volume figures. The August 2025 role reversal and apology were reported by ABC News.
• Klarna press release, 27 February 2024, published with OpenAI: the "700 full-time agents", 2.3 million conversations and resolution-time claims. The May 2025 reversal and CEO quotations were reported by CX Dive and Bloomberg.
• Google Cloud round-up, 22 April 2026: single source for the Capcom, Tata Steel and Home Depot rows. Its Virgin Voyages and Citadel Securities figures refer to production timelines and TPU research workloads, not to agents.
• Microsoft Cloud blog, 24 February 2026 (Far EasTone) and Microsoft WorkLab, 13 November 2024 (Dow). Both vendor-published customer stories.
• Reuters, 5 May 2025 (JPMorgan Coach AI); CNBC, 11 July 2025 (Goldman Sachs and Cognition's Devin); Healthcare Dive, 17 June 2025 (Universal Health Services). Each reports company-supplied figures without auditing them.
• Uber Engineering on Finch, 17 July 2025 (no outcome metric published); Salesforce help centre, 4 December 2024; IBM AskHR case study (undated, 2024-vintage data); Walmart newsroom, 24 July 2025.