What is an example of an agentic AI?

What Makes AI 'Agentic' — and Why Most Definitions Miss It

Every AI vendor now claims their product has "agents," yet most of what's on the market is still a chatbot wearing a new name badge. If you've felt confused trying to tell the difference, you're in good company — even the adoption numbers conflict wildly, with one survey finding 79% of executives say agents are being adopted, while Capgemini research puts actual deployment at just 14%.

The clearest definition comes from the labs building these systems. OpenAI's practical guide defines agents as "systems that independently accomplish tasks on your behalf" — and explicitly states that simple chatbots and single-turn LLMs are not agents. Anthropic's engineering team describes agents as "typically just LLMs using tools based on environmental feedback in a loop." Strip away the marketing, and both converge on the same test.

Here's the test you can apply to any tool claiming to be "agentic":

• Does it plan and select tools dynamically, rather than following a fixed script?
• Does it run in a loop — acting, observing the result, and self-correcting — until an exit condition is met?
• Does it complete multi-step tasks across systems, returning control to a human only when it finishes or fails?

A chatbot answers your question and stops. An agent decides what to do next, takes the action, checks whether it worked, and tries again if it didn't. That loop is what separates genuine autonomy from a fancy autocomplete.

This distinction matters because real autonomy is rarer than the hype suggests. Only 7% of organizations have reached the "Operationalizing" stage where autonomous decision-making produces significant ROI, and 40% of those surveyed say more than 40% of AI pilots stall before production because infrastructure isn't ready for autonomy. Most "agents" being sold today are still assistive copilots — helpful, but not independent.

The gap also has a security dimension. IBM's security guidance warns that once an agent uses tools, "it is no longer just generating text, it is interacting with the outside world" — and recommends least-privilege access and permission-gating on every tool call. That's why SkillzBench is building its directory around verified, version-pinned, security-scanned agent skills, so you can see what a skill actually does before you give it a loop to run in.

Keep the loop test handy. When you meet a real agent later in this article, you'll recognize it instantly.

Six Real-World Agentic AI Examples and What They Actually Do

The clearest way to understand agentic AI is to look at systems already handling real workloads — not answering questions, but completing tasks end to end. The examples below are among the most documented deployments, though it's worth noting upfront that most metrics come from the vendors and customers themselves rather than independent audits.

Klarna's customer service assistant is perhaps the most-cited example. Built with OpenAI, it handled 2.3 million conversations in its first month — about two-thirds of all chats — and cut average resolution time from 11 minutes to under 2, according to industry analysis. Klarna credits it with work equivalent to 700 full-time agents. What makes it agentic rather than a chatbot is the end-to-end resolution: it processes refunds, returns, and cancellations autonomously, escalating only when needed.

Cox Automotive's FleetMate shows multi-agent orchestration in a physical-industry workflow. Running on Amazon Bedrock, FleetMate cut vehicle repair-estimate turnaround from 8–48 hours to 30 minutes, and Cox projects one automation alone will save 17,000 work hours. The company now runs 17 agentic solutions in production.

Other named deployments illustrate different flavors of autonomy:

• Anthropic's coding agent solves real GitHub issues in SWE-bench Verified from the pull request description alone, because code can be verified through automated tests.
• Thomson Reuters' legal assistant reduces time spent on document review, research, and drafting by an average of one-third.
• Team Liquid's SAP Joule agents analyze over 10 million games and 1.6 TB of esports data, saving roughly $250,000 annually in analyst hours.
• Gamma's Intercom Fin resolves 75% of inbound customer conversations end to end — over 18,000 resolutions per month — with manual handling dropping from 94% to 24%.

Each example demonstrates the same core mechanic: an LLM using tools in a loop, planning steps, acting on feedback, and stopping when the task is done — as Anthropic's engineering team describes it. The difference between these systems and a chatbot isn't intelligence; it's the ability to act, self-correct, and finish.

Treat these figures as directional rather than precise — they're vendor-reported, and definitions of "resolution" or "deployment" vary. Still, the pattern is consistent: agents deliver most where tasks are verifiable and bounded, like customer service and coding. For teams building their own agents, the same principle applies to the skills and tools you attach — vetted, tested capabilities matter more than raw model power, which is exactly the gap SkillzBench's free skills directory and benchmarking are being built to close.

The Autonomy Gap: High Adoption, Low Maturity

Here's the uncomfortable truth about agentic AI in 2025: nearly everyone is adopting it, but almost nobody has mastered it. According to a PwC survey of US executives, 79% say AI agents are already being adopted in their companies — yet only 7% of organizations have reached the "Operationalizing" stage, where autonomous decision-making actually starts producing significant ROI.

That gap between enthusiasm and execution is the defining tension of this technology right now. While 62% of organizations are experimenting and 23% are scaling in at least one function, 68% remain stuck in the Experimenting or Developing phases. And the pipeline leaks badly: 40% of companies report that more than 40% of their AI pilots stall before production, largely because their infrastructure isn't ready for genuine autonomy.

Capgemini's numbers tell an even starker story. Only 14% of organizations have actually deployed AI agents — 12% partially and just 2% fully. The discrepancy with the 79% adoption figure likely comes down to definitions: "adopting" or "piloting" an agent is very different from running one reliably in production.

So where is agentic AI actually working? Two domains stand out:

• Customer service is the largest proven segment at 23.1% of the agentic AI market, with documented wins like Klarna's assistant resolving 2.3 million conversations in its first month.
• Coding is the other mature domain — 86% of organizations now deploy AI coding agents for production code, because code can be verified automatically through tests.
• Multi-agent systems are becoming the dominant architecture, holding a 66.4% market share, with 57% of organizations using agents for multi-stage workflows.

Anthropic's engineering team explains why these two domains lead: agents succeed when task outcomes are verifiable — a resolved ticket or a passing test suite provides clear feedback. Domains without such clear success signals remain harder, which is why most other use cases are still experimental.

The architecture is also evolving. Google Cloud describes enterprises moving from single assistants to specialized agents that orchestrate entire workflows — supply chain agents talking to compliance agents, which then trigger financial forecasting agents. Cox Automotive, for example, runs 17 agentic solutions in production, including FleetMate, which cut repair-estimate turnaround from 8–48 hours to 30 minutes.

For teams navigating this maturity gap, the practical challenge is knowing which agent capabilities are trustworthy enough to deploy. That's exactly the problem SkillzBench is being built to solve — a free directory that security-scans, version-pins, and benchmarks agent skills in a sandbox so you can see which ones actually lift performance before anything touches production. In a market where pilots stall 40% of the time, verifying your building blocks is the cheapest insurance available. You can join the waitlist at SkillzBench to get access when it launches.

The Hidden Cost of Autonomy: Security Risks Nobody's Ready For

The moment an agent picks up a tool — a database, an email account, a payment API — it stops being a text generator and starts being an actor in your business. That shift is exactly where the risk lives, and industry analysis is blunt about it: security becomes fundamentally more complex once agents move from generating information to taking actions.

The numbers behind that warning are sobering. According to adoption research, only 2% of enterprises use identity security tools actually built for AI agents — even though agents already make up 22% of all non-human accounts in organizations. In other words, we're giving autonomous software real permissions and real credentials faster than we're building the locks.

Three failure modes dominate the threat picture. IBM's agent security guidance identifies the patterns every adopter should understand:

• Prompt injection — malicious content hidden in web pages, emails, or documents the agent reads, hijacking its instructions mid-task.
• Over-permissioning — granting agents broad access "just in case," turning one compromised agent into a system-wide breach.
• Data poisoning and adversarial inputs — corrupting the information an agent relies on to make autonomous decisions.

The guidance for defending against this is refreshingly unglamorous. IBM's principle is "never trust, always verify": every tool invocation should be permission-gated and audit-logged, and, in the words of IBM's security tutorial, "under no conditions should you give an agent or tool root access to your system. Least privilege enforcement is crucial." An agent that files expenses doesn't need access to payroll. An agent that drafts emails doesn't need to send them unsupervised.

The encouraging news is that organizations are waking up. The same research found that 49% of organizations now define high-risk scenarios where autonomous agents aren't allowed access — up from 28% just a year earlier.

For teams extending agents with third-party tools and skills, vetting is the practical frontline defense — and it's where published research raises a flag worth heeding: roughly 26% of agent skills carry at least one vulnerability. That's why SkillzBench is building its directory around verification — security-scanning every skill, pinning each version to a commit SHA, and benchmarking in a sandbox before anything reaches your agent — plus a free MCP connector that lets you toggle exactly which skills and tools each agent can see, so least-privilege isn't aspirational, it's the default.

The lesson from the security research is simple: treat every agent like a new employee with keys to your systems. You wouldn't hand a new hire admin credentials on day one — don't hand them to an agent either.

How to Put Agentic AI to Work (Safely) in Your Own Workflow

The fastest way to get burned by an AI agent is to give it everything on day one. The safest path — and the one both OpenAI and Anthropic recommend — is to start with a single agent on one well-defined, verifiable task, then expand deliberately. Anthropic notes that most successful agent implementations used simple, composable patterns rather than complex frameworks, precisely because agents trade latency and cost for task performance.

Pick a task where success is easy to check. Coding is the classic example: Anthropic's agent can solve real GitHub issues from a pull request description alone because automated tests verify the result. A monthly report, a data cleanup, a triage workflow — anything with a clear "done" state works. A chatbot that just drafts text isn't an agent; an agent runs in a loop, uses tools, and acts until the task is complete.

Then add tools one at a time, and gate every permission. IBM's security guidance is blunt: "Under no conditions should you give an agent or tool root access to your system." Every tool invocation should be permission-gated and audit-logged, following a "never trust, always verify" principle. This matters more than most teams realize — research shows only 2% of enterprises use identity security tools built for AI agents, even though agents now make up 22% of all non-human accounts.

Before handing your agent new capabilities, run through a short checklist:

• Is the task verifiable? If you can't check the output automatically, keep a human in the loop.
• Does each tool have the minimum access needed — no root, no blanket permissions?
• Have you defined high-risk scenarios where the agent isn't allowed at all? Only 49% of organizations have, though that's up from 28% a year earlier.
• Are you testing in a sandbox before production? Anthropic recommends extensive sandboxed testing and guardrails.

Skills and extensions are where caution pays off most. Agent skills — instruction packages that extend what Claude Code, Codex, Cursor, and similar agents can do — are proliferating fast, but published research found that roughly 26% of published skills carry at least one vulnerability. Installing one blindly is like giving a stranger keys to your workflow.

That's the problem SkillzBench is being built to solve: a free, independent directory that security-scans skills, pins every version, benchmarks them in a sandbox against the same model with and without the skill, and publishes the measured lift separately from trust scores. A free MCP connector lets you toggle exactly which skills and tools each agent sees — nothing more. The directory and connector are both free; you can join the waitlist now to vet skills before your agents ever touch them.

Start small, verify everything, and expand only what you can audit. That's how the 7% of organizations actually operationalizing agents got there — deliberately, not all at once.