By Ryan Kowalski. Originally published on Meikuio on July 18, 2026. Updated for this edition on October 18, 2026.
AI agent examples include Klarna’s customer-service assistant, coding tools such as Devin, and John Deere’s weed-targeting machinery. They do different jobs and have different levels of autonomy. The useful question is what a named system does in a real workflow, what evidence supports the result, and where a person still makes the decision.
Dated company figures below are historical claims, not independently audited measurements of current performance.
What is an AI agent, in one example?
An AI agent works toward a goal across several steps, using tools or taking actions as part of the process. A refund workflow might read an order, check a policy, propose or issue a refund within its permissions, and update the record. The label does not tell you how much of that workflow runs without review.
That distinction matters because vendors apply the word agent to different systems. Some are chat interfaces over existing automation. Others choose tools and actions dynamically. A deployed product can be useful without being fully autonomous. Check its permissions, stopping conditions and escalation path before comparing it with another example.
AI agent examples that are actually running in production
The examples below have named products or operators. That makes them more concrete than an anonymous demo, but it does not make their success rates comparable. Company reports, vendor case studies and independent experiments are different kinds of evidence. Keep the date, task and denominator attached to each number.
Company reports describe particular deployments, not comparable success rates. Table made with Meiku Charts, reviewed October 18, 2026.
These systems span assistants, coding agents and task-specific physical AI. Table made with Meiku Charts.
The clearest examples have bounded jobs. A support system can answer routine questions while escalating exceptions. A coding agent can work on an issue while a developer reviews the patch. Success in either case does not establish that the same product can run an entire department. Narrow scope makes the result easier to inspect.
Resolution rate needs a definition. It can refer to the subset of conversations offered to the agent, exclude certain requests, or use containment as a proxy for a successful answer. Ask whether the customer’s problem was solved, whether the customer came back, and how escalation was counted. A vendor-wide average is not a forecast for your own help desk.
Coding has unusually useful independent evidence. METR’s July 2025 randomized study found that experienced contributors took 19% longer with the early-2025 AI tools tested on their own repositories, despite expecting a speedup. The study involved 16 developers and 246 tasks. That is evidence about a specific setting and period, not a verdict on every coding agent or on October 2026 tools. METR’s February 2026 follow-up describes why selection effects and changing usage make a new estimate difficult.
The examples vendors show you (and what they leave out)
Vendor example pages often describe capabilities such as invoice processing or ticket resolution without identifying an operator, deployment date or measured result. Such a page can explain what a product is designed to do. It does not, by itself, show that a customer is using the entire workflow successfully.
Before treating a scenario as a deployed system, distinguish what has been demonstrated from what has been proposed. These common examples need that check:
A product-development agent that researches trends, checks rules and proposes formulations may be a concept or prototype. Look for a named operator and a record of the decisions it can actually execute.
A fully autonomous farm is a much broader claim than a machine that performs one field task. Verify which operations are deployed, which are supervised and which remain a proposed system.
A student or research prototype can demonstrate a mechanism without establishing a supported commercial product. Its evaluation conditions matter more than the label on a list of examples.
Concepts and prototypes can be useful, but they should be identified as such. A public demonstration, a paid pilot and a production deployment answer different questions. Ask who operates the system, since when, on which tasks, and with what oversight. Missing evidence is a reason to withhold a conclusion, not proof that the idea cannot work.
Klarna: workload, savings and human support
Klarna’s published workload figure makes it a useful case for separating volume, savings and service quality.
In February 2024, Klarna reported that its assistant had handled 2.3 million conversations in its first month, about two-thirds of its customer-service chats. The announcement equated that workload with 700 full-time agents and projected a $40 million profit improvement for 2024. Those were company claims. The profit figure was a projection, and workload equivalence was not a count of people dismissed.
Bloomberg reported on May 8, 2025 that Klarna planned more human customer-service options after emphasizing AI-led cost cuts.
The customer-service question does not end with a workload figure. Klarna’s published support and complaints routes still include human contact. A useful assessment has to measure quality, repeat contacts and escalation as well as cost. Keeping people available can be part of a working deployment, rather than evidence that the underlying automation was never useful.
The same test applies to an AI app-builder or outsourced development service. Ask which work the software performs, which work people perform, and how the handoff is disclosed. A polished interface does not establish autonomous execution. Buyers need a reviewable record of the work and a clear account of who is responsible when it goes wrong.
How many of these actually work?
Case studies establish that particular workflows exist. They do not establish the success rate of all agent projects, because unsuccessful or unfinished projects are less likely to become public examples.
Gartner, in June 2025, projected that more than 40% of agentic AI projects will be canceled by the end of 2027, citing rising costs, unclear value, and weak risk controls. Gartner also reckoned that of the thousands of vendors claiming to sell “agents,” only around 130 were doing something real, and called the rest “agent washing.”
McKinsey’s November 2025 survey separates adoption stages: 23% of respondents said their organizations were scaling an agentic AI system somewhere, and another 39% said they were experimenting. Together that is 62% reporting some agent use. The survey’s roughly 6% AI high-performer group met a broader definition involving significant value and at least 5% EBIT impact from AI. It is not the share of companies making any profit from agents, and it is not a conversion rate from the 23% group.
Share of survey respondents, November 2025. Any use includes experimentation and scaling, so the bars overlap. Chart made with Meiku Charts.
The two bars overlap: organizations scaling agents are included in the 62% reporting any use. They are not slices of a pie. The separate high-performer measure covers AI more broadly and is deliberately not plotted as an agent-profit result.
For coding, benchmark scores and workplace productivity measure different things. A repository task in a study includes context, review and integration that a headline benchmark may not capture. Measure completion time, correctness, rework and review effort on your own tasks. A rising score does not remove those costs from a production workflow.
The working examples remain useful. Their limits are part of the example: what was attempted, what was counted, and how much supervision the result required. A list that includes only successful vendor case studies cannot tell you how often a new project will succeed.
AI agent examples you can actually run yourself
A small business can start with a bounded workflow rather than copying an enterprise deployment. The practical examples below are jobs to test, not a promise that installing an agent will make them reliable automatically.
It reads your incoming mail, sorts it, and drafts replies you approve before they send (email triage).
It sits on a call, transcribes it, and pushes action items straight to your task list (meeting notes).
It scores inbound leads against your criteria and fills in the missing contact details (lead qualification).
It answers routine customer questions from your help docs before a human sees them (support deflection).
It pulls line items out of invoice PDFs and files them as structured records (data extraction).
You can use a purpose-built application or connect tools in a workflow builder. A ready-made product can reduce setup time, while a custom workflow gives you more control over the steps. Compare usage fees, maintenance and review time. A visual builder is not automatically cheaper once those costs are included.
Set approval rules before connecting an agent to customers or payments. For example, it can draft a reply or propose a refund, then wait for a person to approve the action. Record failures and escalations alongside completed tasks. Start with one job whose output you can check, then expand only when the results support it.
Frequently asked questions
What is an example of an AI agent?
A support agent is a clear example when it can read a request, consult account information and take a permitted action. A system that only answers from help documents is a narrower assistant. Klarna and Intercom provide deployed support examples, while coding agents and task-specific physical AI illustrate other forms of action.
What is the most successful real AI agent example?
There is no defensible single winner across support, coding and physical machinery. They do different jobs, and most public success rates are company claims with different denominators. Choose a measure for the task: verified resolution, accepted code, reduced rework or an observed field outcome.
Are AI agents actually being used in 2026, or is it hype?
Deployed products exist, including the support and coding systems described here. Their existence does not establish the success rate of the wider market. Gartner’s 2025 cancellation forecast concerns projects through the end of 2027, not an observed count of failures today. A pilot should be evaluated separately from a production rollout.
What’s the difference between an AI agent and a chatbot?
A conversational interface can answer a question without taking an action. An agentic workflow uses tools or other steps to pursue a goal within its permissions. The two can share the same chat interface, so inspect the workflow rather than relying on the product’s name. Human approval can still be part of an agent’s design.
Can a small business actually use an AI agent?
Yes, for bounded jobs such as email triage, meeting notes, lead qualification, support and invoice extraction. Start with a task whose outputs you can verify. Include approval for consequential actions, track errors and review effort, and compare the result with the process you already use.
Do AI agents really save money?
They can, but savings need to be measured after software fees, setup, upkeep, errors and human review. A vendor’s resolved-conversation rate is not a profit calculation. McKinsey’s 6% high-performer figure is a broader AI category, not a measured percentage of companies earning any return from agents.
My Take
The examples are most useful when they show a bounded job and leave enough evidence to examine the result. A support reply, a code patch or an invoice record can be checked. A claim that an agent will run an entire operation needs much more evidence than a successful demonstration.
I would choose one repetitive job, record how long it takes today, and test the agent against that baseline. Count the time spent checking and fixing its work. Keep a person responsible for actions involving customers or money. Expand when the record supports it, not when a vendor’s example happens to look like your business.
Sources and measurement notes
Klarna’s 2024 announcement · McKinsey’s 2025 survey · METR’s 2025 controlled study · METR’s 2026 follow-up · Bloomberg’s May 2025 report · Salesforce’s Help-site case study · Sierra’s WeightWatchers case study
Other company records reviewed include Morgan Stanley’s June 2024 Debrief announcement, Commonwealth Bank’s 2026 half-year presentation, current coding-product documentation, and John Deere’s See & Spray documentation. Product capabilities and historical reported results are not independent audits.
The original Meikuio article contains the interactive version.
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