7 AI agents examples that actually work (and what they cost)

AI agents examples that actually work in production today: Klarna’s customer service agent, Uber’s Finch, GitHub Copilot, Ramp’s transaction matcher, and a handful more. Each one uses tools, runs in a loop, and finishes a real task without a human babysitting every step. This is what agentive AI looks like when it actually ships. If you want a primer on the different types of AI agents first, I mapped that out separately. And if you’re specifically looking at the agentic subset — agents that plan, pick tools, and run multi-step tasks on their own — I sorted those by production-readiness in agentic AI examples. (For the full picture of what’s happening in this space, see the AI agents and agentic systems hub.)

The honest version of this list is less glamorous than you’d think. 88% of AI agent projects never reach production (meaning: never make it to real daily use). The ones that do ship tend to be boring, narrow, and cheaper than you’d expect. The flashy “autonomous everything” demos? Most of those are still demos. The principles behind what makes these agents work come down to restraint: narrow job, clean tools, human checkpoints.

Real AI agents examples that work today

Before we get into each one, here’s the quick comparison. Every example below includes what it does, what it replaced, and what it actually costs to run.

The details matter more than the table, though.

Research and data retrieval

Uber’s Finch agent lives inside Slack. Financial analysts at Uber used to write manual queries every time they needed a number. Now they ask Finch in plain English, and it pulls the data from internal databases.

The boring part is the point: Finch doesn’t make decisions. It looks things up. That’s exactly why it works. The narrower the task, the less that can go wrong. Evidently AI documented this deployment alongside nine other real company examples.

Customer support triage

Klarna’s AI assistant is the most-cited AI agent success story. And also the most misunderstood.

The numbers were real: it handled two-thirds of all customer chats in its first month. Response times dropped 82%. Cost per conversation fell from $0.32 to $0.19. Klarna said the agent did the work of 700 human agents.

Then the CEO reversed course. In May 2025, Klarna reopened human hiring and publicly said they “over-rotated on AI.” The “replaced 700 agents” headline was about avoided hiring, not layoffs. And the quality issues that showed up at scale are exactly the kind that narrow metrics miss.

My take: Klarna is the most important AI agent case study because it shows both sides honestly. The cost savings were real. So were the limits. If you’re thinking about hiring an AI agent development company for customer support, start with Klarna’s full story, not just the press release.

Intercom Fin is the quieter version of the same idea. It handles phone support as a voice AI agent, triaging calls before they reach a human. Less dramatic, more sustainable. That’s usually the pattern.

Sales and outreach

Netguru’s Omega agent handles lead qualification and outreach. It reads incoming leads, scores them, and sends the first follow-up automatically.

This is the type of agent that actually makes sense for most businesses. Sales outreach is repetitive, rule-based, and forgettable. Nobody misses writing follow-up emails by hand. The trick is keeping the agent narrow: qualify and route, don’t let it negotiate.

Document processing and compliance

Ramp’s matching agent does something even more boring: it matches transactions to merchants. Before the agent, someone at Ramp did this by hand. Reconciliation is tedious, error-prone, and exactly the kind of task where an agent shines, because the stakes per individual action are low.

AtlantiCare’s clinical agent is the healthcare version. It reduced documentation time by 42% and saved clinicians 66 minutes per day. Doctors spend too much time typing and not enough with patients. This is one of those cases where the agent genuinely helps, because the alternative was burnout.

Knowledge and productivity

Dropbox Dash helps knowledge workers search across all their apps at once. Instead of opening Slack, then Google Drive, then Notion, then your email, you ask Dash and it pulls results from everywhere.

Airtable’s Field Agents do content summarization. You point them at a set of records and they summarize each one automatically.

Both of these are “safe” agent use cases. They read data. They don’t change anything. That read-only pattern is the easiest way to get an agent into production without worrying about it doing something destructive. If you want to browse pre-built agents in a marketplace instead of building from scratch, I wrote a buyer’s guide with honest cost comparisons.

Code and development

GitHub Copilot is probably the most mature AI agent in production. Millions of developers use it daily. It suggests code, completes functions, and runs multi-step tasks using tools.

The progress is wild. On the SWE-bench test (a standard benchmark for coding agents), task completion went from near-zero to 74.4% by end of 2025. Coding is where agents work best, because code is testable. You can check if the output is correct. That’s harder with a customer support response.

My take: If you’re looking for your first AI agent to try, start with a coding assistant. The feedback loop is the fastest, the tools are the most mature, and you can check the results immediately. Then work outward to building your own AI tools for other tasks.

What each example actually costs to run

Cost matters more than the demo. And most people skip it.

AI agents don’t just send one message and get one reply. They loop. A simple agent that calls tools might use 5,000 to 15,000 tokens per task. A more complex setup where multiple agents hand work to each other can burn 200,000 to over 1,000,000 tokens per task.

Think of a token as roughly one word. More tokens, more money.

Token prices dropped about 67% year-over-year. Companies still went 3x over their AI budgets. Cheaper tokens meant people used way more of them. One study found a 18.6x jump in per-developer AI consumption. It’s like gas getting cheaper, so everyone drives twice as much.

A realistic rule of thumb: take the base API cost (what you pay the AI provider per request) and multiply by 1.7 to 2.0x. That covers retries, errors, and the back-and-forth of tools calling each other. If someone quotes you just the API price, they’re giving you the best case.

For most small businesses, a single focused agent running low-code automation will cost $50 to $200 per month in API fees. The expensive part isn’t the AI. It’s the building and debugging.

Why most AI agent projects never ship

Every data source says roughly the same thing:

• 88% of AI agent projects never make it to production (Digital Applied, 2025)
• Gartner predicts 40%+ of agentic AI projects will be canceled by 2027 (Gartner, 2025)
• Only 5.5% of organizations see real financial returns from AI (McKinsey State of AI, 2025)
• Only 2% of firms have fully scaled agent deployments (Capgemini, 2025)

The one that surprised me: enterprise trust in fully autonomous agents (AI running without human oversight) dropped from 43% to 27% in a single year. Companies tried agents, saw the problems, and pulled back.

Three things kill agent projects:

1. Started too big. Built a multi-agent system when a single-step automation would’ve done the job. Anthropic’s own research found that “the most successful implementations weren’t using complex frameworks.” Check which agentic AI framework actually fits before building.
2. Skipped the testing. No evaluation plan before launch. The agent worked in the demo but broke on real data. Only 25% of companies moved more than 40% of their AI pilots to production.
3. No governance. (Governance just means: who watches the agent and what happens when it makes a mistake.) Only 21% of firms have mature AI governance. The other 79% are running agents without a safety net.

The latest agentic AI updates show the market is maturing, but slowly. If you’re planning an agent build, start with the smallest version that could work. For more on that approach, I wrote a full guide on how to build AI agents step by step.

How to tell a real agent from a rebranded chatbot

“Agent washing” is everywhere. Gartner coined the term in 2025 after finding that out of thousands of vendors claiming to sell AI agents, only about 130 actually deliver real agent capabilities.

Three questions tell you what’s real:

1. Does it use tools? Can it actually do things (search a database, send an email, call an API), or does it just generate text?
2. Does it loop? Does it keep working until the job is done, or does it give one response and stop?
3. Does it decide its own next step? Does it pick what to do next based on what happened, or does it follow a fixed script?

If all three are yes, you’re looking at a real agent. If not, it’s a chatbot, a workflow, or a marketing slide. That’s fine for some jobs. A chatbot handles FAQ well. You don’t need an agent for everything.

Anthropic (the company behind Claude) published research that basically said: most tasks don’t need an agent at all. A single well-written prompt often does the job. Even Harrison Chase, the CEO of LangChain, admitted their original agent tools didn’t give enough control.

A simpler approach is better when the task is a straight line. If you can describe it as “when X happens, do Y,” you probably need a workflow tool like Make, n8n, or Zapier, not an agent. For a deeper look at AI agentic workflows and how they differ from true agents, I broke down the four main patterns. Low-code automation covers that ground well. For the full breakdown of what makes something “agentic” (meaning: it decides its own next step), see the difference between agentic and generative AI.

My take: I see this all the time. Someone builds an “AI agent” that’s really just a prompt connected to one API call. That’s not an agent, it’s automation with a fancy name. And honestly? It’s often the better solution. Simpler systems break less. Know the difference before you pick an agentic AI framework.

The safety gap

The MIT AI Agent Index studied 30 deployed agents and found:

• 25 out of 30 disclose no internal safety results
• 23 out of 30 have zero third-party testing
• Most have no public evaluation of what happens when they fail

A separate study of 14,000 real agent sessions found:

• 38% of sessions saw the agent go beyond its assigned scope (doing things it wasn’t supposed to)
• 12% fabricated success (reported the task was done when it wasn’t)

One real cautionary tale: an AI coding agent (running through Cursor) deleted a user’s production database. The live one. With real customer data.

Research on the Vending-Bench test showed that agents tend to “spiral rather than self-correct” on long tasks. Instead of realizing they’re stuck, they keep trying the same broken approach over and over. Like a GPS that keeps telling you to make a U-turn into a wall.

None of this means agents are useless. It means they need boundaries, testing, and a human checking the output. The seven examples above all share one thing: they’re narrow enough that someone can spot-check the results.

If you’re implementing AI in your business, treat an agent like a new hire who’s brilliant but unpredictable. Give them one clear job. Check their work. And don’t hand them the keys to production on day one.

How I can help

Most of the people I talk to don’t need a multi-agent system. They need one focused agent doing one boring task really well. The kind of thing that saves 10 hours a week and pays for itself in the first month.

If you’re wondering which of these AI agent examples fits your business, or whether you need an agent at all, book a free 15-minute call. No pitch, no slides. Just a conversation about what would actually help. Sometimes the answer is “you don’t need an agent, just use Make.” I’ll tell you that too.

FAQ

What is an example of an AI agent?

Klarna’s customer service agent is the most well-known. It handled two-thirds of all customer inquiries, cut response times by 82%, and lowered the cost per conversation from $0.32 to $0.19. Other examples: Uber’s Finch agent (financial data retrieval), GitHub Copilot (code completion with tool use), and Ramp’s transaction matching agent. The common thread is that each one uses tools, loops, and decides its own next step, rather than just answering one question at a time. For a deeper look, the best AI agents worth trying covers tools you can actually use today.

Who are the Big 4 AI agents?

There’s no official “Big 4.” The most widely deployed agent platforms right now are Microsoft Copilot, Salesforce Agentforce, Google Gemini agents, and OpenAI’s Agents SDK. But “Big 4” is a marketing label, not a ranking. The right platform depends entirely on what system you already use and what task you’re trying to automate. For most small businesses, AI platforms for business is a more useful starting point than brand names.

What are the 5 types of AI agents?

The textbook answer (from Russell and Norvig’s AI textbook) is: simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents. But in practice, nobody builds using those categories. Real agents are classified by what they do: customer support, research, code, sales, and operations. The academic taxonomy is useful for exams. The job-based classification is useful for building your own AI tools.

Is ChatGPT an AI agent?

ChatGPT by itself is a chatbot. You ask, it answers. But ChatGPT with plugins, code interpreter, or web browsing starts acting like an agent because it uses tools and loops. The line between chatbot and agent is blurry, and it depends on what features you turn on. The clearest test: can it call tools, loop, and pick its own next step? If yes, it’s behaving as an agent. If it just replies to your message, it’s a chatbot. The difference between agentic and generative AI breaks this down further.

How much does it cost to run an AI agent?

It depends on complexity. A simple tool-calling agent costs about $0.01 to $0.05 per task. A complex multi-agent system can cost $1 to $5+ per task. The real surprise: token prices dropped 67% in the last year, but most companies still blew their AI budgets because agents use 5 to 30 times more tokens than a chatbot. For a small business running one focused agent, expect $50 to $200 per month in API costs. The expensive part is building and debugging, not the ongoing fees. If you’re considering a build, what AI consulting involves can help you figure out the full picture.