What exactly does an AI agent do?

Why "AI agent" confuses everyone (and why chatbots aren't agents)

Search for "what is an AI agent" and you'll get a dozen confident answers that don't quite agree. The Frontier Model Forum — an industry body — openly notes the term "does not yet have a settled definition," so vendors, articles, and tools use it inconsistently, often slapping the "agent" label on anything with a chat box.

The confusion is understandable given the pace. According to the MIT AI Agent Index, 24 of 30 prominent agents launched or received major agentic updates in just 2024–2025, and Google Scholar papers mentioning "AI agent" in 2025 exceeded all prior years combined. The word outran the definition.

Here's the cleanest line in the literature. OpenAI's practical guide is blunt: "Applications that integrate LLMs but don't use them to control workflow execution—think simple chatbots, single-turn LLMs, or sentiment classifiers—are not agents." The test isn't whether something talks; it's whether the model decides what happens next.

IBM draws the same line from the security side: "Once tools are added, the agent is no longer just generating text, it is interacting with the outside world." A chatbot produces words. An agent takes actions — calling tools, querying services, changing state in external systems.

So when a product claims to be an "agent," apply this functional test:

• Does the LLM control the workflow, deciding which steps run and in what order — or does fixed code always run the same path?
• Can it call external tools to gather context and take actions, not just generate text?
• Does it operate in a loop — planning, acting, observing results, and adapting — until the goal is met?
• Does it maintain memory and state across steps, rather than resetting every turn?

If the answer to the first question is no, you're looking at a chatbot with good marketing. If it's yes, the agent's real capability is defined by its tools — which is also where the risk lives. The Frontier Model Forum puts it plainly: every tool an agent can access expands both its capability and its exposure, and MIT found 25 of 30 agents disclose no internal safety results at all.

That's why curation matters as much as capability. SkillzBench is building a free directory that security-scans, verifies, and benchmarks agent skills before you equip them — so the agent only sees tools that were deliberately chosen, not whatever happened to be lying around.

The four things an agent actually does: plan, act, remember, adapt

An AI agent is software that uses a large language model to pursue a goal over time — not just answer one question, but plan, act, observe, and keep going until the job is done or it hands control back to you.

Strip away the hype and every AI agent is doing the same four jobs, whether it's a chatbot that books meetings or a coding assistant that ships a pull request.

1. Plan. The agent breaks a goal into steps and decides what to do first. Neo4j's Jesús Barrasa frames this as the start of the agent's execution loop: planning steps, retrieving information, calling tools, observing results, and adapting as new context appears.

2. Act. The agent calls external tools to do real work — querying a database, sending an email, running code, browsing a page. OpenAI's practical guide is explicit that this is what separates an agent from a chatbot: once the LLM controls workflow execution and invokes tools, it has crossed the line.

3. Remember. The agent holds state across steps and, in many systems, across sessions. This is what lets a multi-step task survive a failure, a timeout, or a follow-up question from the user.

4. Adapt. When a tool returns an error or new information changes the picture, the agent revises its plan and tries again. The Frontier Model Forum notes that the most capable agents "can now reliably plan and execute long sequences of actions autonomously, invoking tools, querying external services, maintaining memory across sessions, and interacting with other agents."

Not every agent runs itself. MIT's AI Agent Index documents autonomy on a 1–5 scale: chat agents typically operate at Levels 1–3 (turn-by-turn with a human in the loop), browser agents reach Levels 4–5 (longer runs with limited mid-execution intervention), and enterprise agents are often configured conservatively at Level 1–2 but deployed at Level 3–5, triggered by events without a human watching.

The field is consolidating around four common shapes, and recognizing them helps you pick the right tool for the job:

• Chat agents — conversational assistants that execute code and connect to third-party services (the largest category in MIT's index, at 12 of 30 sampled agents).
• Browser agents — navigate the open web, fill forms, and extract data from sites that don't offer APIs.
• Coding agents — operate inside developer environments (IDEs, terminals, repos) to write, test, and ship code.
• Custom and enterprise agents — built via SDKs with bespoke tool sets, tuned to a specific business workflow.

The thread tying all four shapes together is tool access — and that is also the risk surface. Every skill, connector, or integration an agent can reach expands what it can do and what it can touch. With published research finding that roughly a quarter of community-published agent skills carry at least one vulnerability, choosing what you equip matters as much as choosing the model. SkillzBench is building a free, independent directory that verifies, security-scans, and benchmarks skills before you load them, with a free MCP connector so you only hand your agent the tools you have actually approved.

Skills, proven. Browse the free SkillzBench skill directory and connect any agent with our free MCP — built on published security research showing ~26% of agent skills carry at least one vulnerability, so every skill is verified, scanned, and benchmarked before you equip it. Join the waitlist.

Your agent is only as good — and as safe — as the tools you give it

Every tool you hand an agent is a double-edged sword. The same connection that lets it book meetings, query databases, or deploy code also opens a new door into your systems — and the industry is only starting to grapple with what that means.

The Frontier Model Forum puts it bluntly: "Every tool an agent can access expands both its capability and its exposure." IBM's security guidance goes further, warning that "every tool is a potential attack surface" and that unrestricted tool access is one of the most common vulnerabilities in agent systems. The moment tools enter the picture, an agent stops generating text and starts acting on the outside world.

Yet the answer isn't to starve your agent of tools. OpenAI's practical guide notes that well-structured implementations can successfully manage more than 15 well-defined, distinct tools — while poorly organized ones struggle with fewer than 10 overlapping tools. Structure, not scarcity, is what separates a capable agent from a chaotic one.

The uncomfortable part is that safety transparency lags far behind capability. MIT's AI Agent Index found that 25 of 30 prominent agents disclose no internal safety results, and 23 of 30 have no third-party testing at all. Developers, MIT observes, share far more about what their agents can do than about how safely they do it. And published research on agent skills — the SKILL.md instruction packages that extend coding agents like Claude Code, Codex, Cursor, and Gemini CLI — has found that roughly 26% carry at least one vulnerability.

So what does responsible tool expansion actually look like in practice?

• Verify before you equip — pin each skill to a specific commit, check its license, and run a security scan rather than trusting a README.
• Benchmark with and without the skill, using the same model, so you know the actual Lift rather than guessing at value.
• Scope tools per agent — each agent should see only the tools and skills chosen for it, never everything by default.
• Stay current — track upstream changes to every skill you deploy, re-scan, and re-benchmark when they update.

That verification-and-curation loop is exactly what SkillzBench is building: a free, independent directory that crawls, dedupes, security-scans, and benchmarks agent skills, then connects them to your agent through a free MCP server — the same open protocol that 20 of the 30 agents MIT indexed already support. The real lever for both capability and safety isn't more tools or fewer tools. It's proven tools, deliberately chosen.

When you should (and shouldn't) use an agent

The biggest mistake businesses make with AI agents isn't choosing the wrong model — it's deploying an agent where a simple automation would do. The best evidence suggests restraint beats ambition: Walmart's AdaptJobRec system cut response latency by up to 53.3% while improving recommendation quality precisely because it applied agentic reasoning selectively, not everywhere.

So when does an agent actually earn its keep? OpenAI's practical guide lays out three criteria: the task involves complex decision-making, the rule-based path would be too hard to build and maintain, or the work depends heavily on unstructured data like emails, documents, and free-text tickets. If none of those apply, a chatbot, classifier, or deterministic script is usually the better — and safer — choice.

Even when the criteria are met, agents fail in predictable ways. Neo4j's case studies document the common production failure modes: hallucinations, lost state in long workflows, brittle prompts, and tool misuse — calling the wrong tool, passing incorrect parameters, or ignoring tool results. Notably, when agents fail, it's usually not because the model is incapable; it's because the agent lacks structure in how context, state, and tools are organized.

There's also a security dimension that's easy to underestimate. IBM's security guidance is blunt: "never trust, always verify" — treat every tool as untrusted until validated, and under no conditions give an agent or tool root access to your system. The Frontier Model Forum makes the stakes clear: every tool an agent can access expands both its capability and its exposure, and potential damage scales directly with the permissions you grant.

A practical checklist before you deploy:

• Confirm the task genuinely needs multi-step judgment and tool access — not just text generation.
• Scope tool permissions to the minimum the task requires, and never grant root-level access.
• Plan for state loss in long workflows, and build checkpoints where a human can step in.
• Vet every skill or tool package you attach — published research suggests roughly 26% of agent skills carry at least one vulnerability.

That last point is where much of the risk hides. Since an agent is only as good and as safe as the skills it loads, verifying each one matters. That's the problem SkillzBench is building for: a free directory that security-scans, version-pins, and benchmarks every skill, with per-agent loadouts so your agent only sees what you deliberately chose. If you want to extend an agent with new capabilities, it pays to start with skills, proven.

How to extend your agent with skills you can actually trust

The Model Context Protocol has quietly become the default way to wire an agent to the outside world. According to the MIT AI Agent Index, 20 of 30 prominent agents already support MCP for tool integration, and enterprise agents lead the pack at 12 of 13. That makes skill packages — instruction files that teach an agent how to handle a specific task — the natural next step for anyone who wants to extend what their agent can do.

The problem is trust. Published research has found that roughly 26% of agent skills carry at least one vulnerability. Add that to the Frontier Model Forum's warning that every tool an agent can access expands both its capability and its exposure, and "just install the skill" stops looking like a safe default.

That's where SkillzBench comes in. It's being built as a free, independent directory that treats skill packages the way an app store treats mobile apps: discoverable, verified, and benchmarked before you load them.

Here's what the platform is designed to do for every skill it lists:

• Discover by continuously crawling and deduplicating skills from GitHub across Claude Code, Codex, Cursor, Gemini CLI, and chat-app agents.
• Verify by pinning every version to a commit SHA, running license checks, security scans, and quality judgments.
• Benchmark by testing each skill in a sandbox with the same model, with and without the skill, and publishing the Lift in percentage points.
• Equip through a free MCP server and per-agent loadouts — you toggle which skills and tools each agent sees, and the agent only sees what you chose.
• Stay current by tracking upstream changes, re-scanning, re-benchmarking, and alerting you when something shifts.

A 0–100 trust score pulls together security, quality, license, maintenance, and community signals. Lift is always shown separately from ratings, and money never touches either number — the directory, the MCP connector, the benchmarks, and the alerts are all free, with revenue coming only from advertising around the blog.

For anyone choosing an agent solution, that distinction matters. The MIT index found that 25 of 30 agents disclose no internal safety results and 23 of 30 have no third-party testing. Extending an agent with unvetted skills inherits that opacity. A directory that pins versions, scans for vulnerabilities, and publishes measured performance gives you a way to add capability without quietly adding risk.

Skills, proven. Join the SkillzBench waitlist and browse the free skill directory.