Can you give me some examples of AI agents?

Is ChatGPT an AI Agent? Clearing Up the Biggest Confusion

Ask ten people what an "AI agent" is and you'll get ten answers — most of them describing a chatbot. The terms get used interchangeably so often that even people working with AI daily struggle to explain the difference, which makes it hard to know what's actually worth adopting.

The distinction is simpler than the marketing suggests. OpenAI defines agents as "systems that independently accomplish tasks on your behalf" — and explicitly excludes simple chatbots from that category. Oracle puts it even more directly: ChatGPT doesn't autonomously take actions on a user's behalf. A chatbot responds; an agent plans, uses tools, and completes multi-step work.

That matters because the gap between hype and reality is wide. A PwC survey of 308 US executives found 79% of companies report adopting AI agents — yet only 17% have full adoption across almost all workflows. Knowing what a real agent looks like helps you cut through claims and judge what's genuinely deployed versus what's a chatbot with a new label.

So what separates an agent from a chatbot in practice?

• Autonomy: an agent works toward a goal without needing a prompt for every step.
• Tool use: it can search, execute code, call APIs, or take actions in other systems.
• Multi-step reasoning: it plans a sequence of actions, not just a single response.
• Task completion: it finishes the job — booking, drafting, sorting, executing — rather than just advising.

The nuance is that the line isn't always clean. ChatGPT itself now has agent-like features, and many "copilots" sit somewhere in between. The useful test isn't the product name — it's whether the system independently takes actions on your behalf or just answers questions.

It's also worth being skeptical about what agents are connected to. Security research cited by industry analysts found 74% of IT leaders believe agents represent a new attack vector, and published research found roughly 26% of agent skills carry at least one vulnerability. An agent is only as trustworthy as the skills and tools you give it.

That's the problem SkillzBench is being built to solve: a free, independent directory that verifies, security-scans, and benchmarks agent skills — including a free MCP connector — so you can see which skills actually help before you plug them into an agent. Knowing the difference between a chatbot and an agent is step one; knowing what to trust that agent with is step two.

Real Examples of AI Agents: Three Tiers, From Enterprise to Everyday

The clearest way to understand what AI agents actually look like in practice is to sort the dozens of real examples into three tiers: enterprise platforms, developer frameworks, and named consumer or coding agents. Each tier serves a different buyer, and each comes with its own proof points and caveats.

Tier 1: Enterprise agent platforms. These are pre-built, governed systems for large organizations. Salesforce Agentforce and Microsoft Copilot Studio (roughly $30 per user per month on annual billing) let companies configure agents inside existing workflows, while IBM watsonx Orchestrate ships a catalog of 150+ pre-built automations. UiPath and Google Vertex AI Agent Builder round out the category, and Oracle says more than 50 AI agents are rolling out across its Fusion Cloud Applications Suite at no additional cost — though that figure is self-reported by the vendor.

Tier 2: Development frameworks. For teams building their own agents, the open-source ecosystem shows serious traction. CrewAI has 50,000+ GitHub stars and roughly one million monthly downloads, AutoGen matches it in stars and is used by Novo Nordisk for data science workflows, and LangGraph — credited with helping Klarna cut customer support resolution time by 80% — has 33,000+ stars. Dify tops 100,000 stars, and OpenAI's Agents SDK, released in March 2025, already exceeds 26,000 stars and works with 100+ LLMs. There's tension here worth noting: Forrester predicts 75% of firms attempting DIY agentic architectures will fail, yet these frameworks keep growing. Match the tool to your stack rather than chasing features, as DataCamp advises.

Tier 3: Named agents you can use today. Claude Code, ChatGPT Agent, Devin AI, Perplexity Computer, Manus, and Google Antigravity (launched May 19, 2026, supporting up to 5 parallel agents at $100/month for the Ultra tier) put autonomous task execution directly in individual hands.

Real results are emerging across domains: a multi-agent insurance claims system using seven specialized agents (Planner, Cyber, Coverage, Weather, Fraud, Payout, Audit) cut processing time by 80%, and pathology agents have hit 99.5% accuracy identifying malignant cells. But adoption remains shallow — 79% of companies report adopting AI agents, yet only 17% have full adoption across almost all workflows.

One caution before you plug any agent into your stack: 74% of IT leaders believe agents represent a new attack vector, and published research found roughly 26% of agent skills carry at least one vulnerability. That's why we're building SkillzBench — a free directory that will let you discover, verify, and benchmark agent skills in a sandbox before they ever touch your agents, with a free MCP connector to equip each agent with only the skills you choose. Join the waitlist to get early access.

• Salesforce Agentforce, Microsoft Copilot Studio, IBM watsonx Orchestrate, UiPath, and Google Vertex AI Agent Builder anchor the enterprise tier.
• LangGraph, AutoGen, CrewAI, Dify, and OpenAI's Agents SDK dominate the open-source build-it-yourself tier.
• Claude Code, ChatGPT Agent, Devin AI, Perplexity Computer, Manus, and Google Antigravity bring agents to individual users.
• Klarna's 80% support-resolution improvement and the seven-agent insurance claims system show what deployed agents can deliver.

How AI Agents Actually Work Together: Multi-Agent Systems and the 'Agent Boss'

Look closely at the most impressive AI agent examples and a pattern emerges: behind almost every one sits not a single brilliant agent, but a team of them — and a human who has quietly become their manager. That's the real story of how agents are being deployed in 2025 and beyond.

Multi-agent systems are the emerging frontier. PwC describes them as "systems of AI agents working in concert," spanning finance, customer service, software development, and R&D, according to its survey of 308 US executives. OpenAI's practical guide documents two dominant orchestration patterns: a Manager pattern, where a central agent delegates to specialized agents, and a Decentralized pattern, where peer agents hand tasks to one another. A real insurance deployment illustrates the Manager approach in action — seven specialized agents (Planner, Cyber, Coverage, Weather, Fraud, Payout, Audit) collaborating on claims, cutting processing time by 80%.

Meanwhile, the human role is shifting. Microsoft calls it the "agent boss": people moving from human-in-the-loop to human-on-the-loop — building, delegating to, and supervising agents rather than doing the work themselves. PwC's data backs the scale of this shift: 67% of executives agree agents will drastically transform existing roles within 12 months, and 75% believe agents will reshape the workplace more than the internet did.

Both trends, however, sit atop a sobering paradox. Nearly 8 in 10 companies use generative AI, yet just as many report no significant bottom-line impact — a gap McKinsey calls the "gen AI paradox." Adoption is broad but shallow: 79% of companies report agent adoption, yet only 17% have full adoption across almost all workflows, and fewer than half are redesigning processes around agents.

Expert advice cuts through the hype in two directions:

• Maximize a single agent first. OpenAI's recommendation is explicit: "maximize a single agent's capabilities first" before splitting into multiple — some implementations manage 15+ well-defined tools, while others struggle with fewer than 10 overlapping ones.
• Don't stop at pilots. PwC warns that "companies that stop at pilot projects will soon find themselves outpaced by competitors willing to redesign how work gets done."
• Watch the security surface: 74% of IT application leaders view agents as a new attack vector, and only 13% strongly agree they have proper governance structures.

For the agent boss, this means curating what each agent can see and do — carefully. That's where verified, pre-tested skills matter: SkillzBench is building a free directory of security-scanned, sandbox-benchmarked agent skills plus a free MCP connector, so you can browse proven skills rather than wiring your team of agents to unvetted instruction packages.

The Hidden Risk in AI Agents — and How to Pick Skills You Can Trust

Every example agent we've covered — from Claude Code to multi-agent claims systems — shares one uncomfortable truth: the more autonomy you grant, the bigger the blast radius when something goes wrong. Industry research finds that 74% of IT application leaders now believe agents represent a new attack vector, yet only 13% strongly agree they have proper governance in place.

The incidents back up the anxiety. Security analyses document a Hugging Face breach in which an autonomous agent executed more than 17,000 actions in a single weekend, plus the JADEPUFFER attack — assessed as the first fully autonomous AI-driven ransomware operation. Named agents caught in security incidents include AWS Kiro, Perplexity Comet, Langflow, and Microsoft's Azure DevOps MCP server. None of these were exotic setups; they were ordinary tools doing ordinary work.

The problem is especially acute with agent skills — the instruction packages that teach agents like Claude Code, Codex, and Cursor how to perform tasks. Skills are shared freely on GitHub, but published research has found roughly 26% carry at least one vulnerability. OWASP has responded with the Agentic Skills Top 10 (AST10), the first comprehensive security framework covering the ten most critical risks across all major agent platforms.

So how do you pick skills without gambling? Look for verification at every layer:

• Version pinning — the skill tied to a specific commit, so what you tested is what runs
• License checks and security scans, so you're not importing legal or code risk
• Sandbox benchmarks with and without the skill, so you can see measurable Lift rather than trusting a star count
• Ongoing tracking of upstream changes, with re-scans and alerts when a skill you use gets updated

This is exactly the gap SkillzBench is being built to close: a free, independent directory of agent skills that are version-pinned, license-checked, security-scanned, and sandbox-benchmarked — plus a free MCP connector that lets any agent see only the skills and tools you choose for it. Everything is free, and benchmark results stay separate from ratings so money never touches scores.

The platform is pre-launch, but the waitlist is open now. If you're equipping agents with skills, browse verified ones instead of unvetted ones — your future self, and your security team, will thank you.