What is an agent function in AI?

Why Everyone Is Talking About Agent Functions (And Why Definitions Are Fuzzy)

AI agents have gone from research curiosity to boardroom agenda item in under two years, yet ask ten vendors what an "agent function" actually does and you'll get ten different answers. The term is everywhere in headlines and sales decks, but rarely defined with any precision.

The numbers explain the noise. A Gartner survey of IT application leaders found that 75% of organizations are already piloting, deploying, or have deployed some form of AI agent. Meanwhile, MarketsandMarkets forecasts the AI agent market growing from roughly $5.26 billion in 2024 to $52.62 billion by 2030 — a 46.3% compound annual growth rate.

Behind the hype sits a genuine shift in what buyers are paying for. As Precedence Research puts it, the key shift is from model intelligence to execution capability — better reasoning alone doesn't make an agent commercially valuable. What matters is whether it can plan, call tools, maintain memory, and complete multi-step tasks on its own.

That's where definitions get fuzzy. Anthropic's engineering team, whose work underpins many production agents, offers the clearest framing: agents are "typically just LLMs using tools based on environmental feedback in a loop." In other words, the agent function is the cycle of perceiving the environment, reasoning about what to do next, acting through tools, and observing the result.

The confusion is understandable, because the industry uses overlapping terms for different things:

• Workflows — LLMs orchestrated through predefined code paths, where the developer controls the sequence
• Agents — systems where the LLM dynamically directs its own process and tool usage
• Skills and tools — the capabilities an agent calls during its loop, increasingly connected via protocols like MCP

Why does getting this straight matter for business decisions? Because an agent's function is only as good as the tools it's given, and the tooling layer is where real money moves — MCP SDK downloads grew from roughly 2 million monthly at launch in late 2024 to around 97 million by March 2026, per Precedence Research. Equipping agents with verified, quality-checked skills is becoming a discipline of its own, which is exactly the problem SkillzBench is building a free, independent solution for.

Understanding the agent function — that percept, reason, act, observe loop — is the first step to evaluating any vendor claim on its merits rather than its marketing.

The Agent Function Explained: The Perceive-Reason-Act Loop

An AI agent's function boils down to one deceptively simple idea: a model that perceives its environment, reasons about what to do, acts, and then observes the results — repeating that cycle until the task is done. Anthropic's engineering team captures it plainly: agents are "LLMs using tools based on environmental feedback in a loop."

That loop typically unfolds in four stages. The agent receives a percept (a user command or new data from its environment), reasons about the best next step, takes an action — usually calling a tool — and then observes the outcome, which becomes ground truth feeding the next decision. UiPath describes the same cycle as a system that can "reason, decide, and act" safely within an enterprise environment. The key difference from ordinary software: agent output isn't deterministic, so simply retrying won't guarantee improvement.

What separates an agent from a chatbot or a scripted automation? Anthropic draws a sharp line. Workflows orchestrate LLMs through predefined code paths, while agents dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks. A chatbot answers; an agent plans, acts, checks its work against the environment, and adjusts course. This shift from model intelligence to execution capability is what analysts say makes agents commercially valuable — the ability to plan, call tools, keep memory, and carry out multi-step tasks independently.

The foundation of every agent is what Anthropic calls the augmented LLM — a base model enhanced with three capabilities:

• Retrieval — pulling relevant knowledge from external sources at the moment it's needed.
• Tools — functions the model can invoke: search, code execution, APIs, databases.
• Memory — retaining context across steps so multi-step tasks stay coherent.

One practical caveat from UiPath's agent-building guidance: deterministic tasks like math or date comparisons should be delegated to tools, not left to the LLM to reason through. The model handles judgment; the tools handle precision.

The loop is only as good as the tools wired into it. With tool calling and interoperability now considered essential agent layers — and protocols like Anthropic's MCP connecting agents to external tools and data — the quality and safety of those connections matter enormously. That's exactly the problem SkillzBench is being built to address: a free directory and MCP connector for discovering, verifying, and equipping agent skills, with sandboxed benchmarking and security scanning, currently in development ahead of its launch.

Seen in production, the loop delivers real results. Foxconn and BCG's agent ecosystem now automates 80% of decision-making processes, and Fujitsu's supply chain agents cut warehousing costs by $15 million while halving staffing needs. In healthcare, Epic launched three agents — Art for documentation, Penny for billing, and Emmie for patient questions — each running its own perceive-reason-act cycle inside clinical environments. The agent function, in short, is what turns a language model from something that talks into something that works.

Agent Functions in the Wild: What the Loop Accomplishes in Practice

The gap between a clever AI demo and a working AI system comes down to one thing: whether the agent function actually runs, reliably, in production. And in 2025 and 2026, it increasingly does — at scales that make the percept–reason–act loop look less like theory and more like infrastructure.

Consider healthcare. Epic Systems has launched three agents built directly into clinical workflows: "Art" handles note-taking and documentation, "Penny" manages bill collection and coverage denials, and "Emmie" answers patient questions and handles scheduling, according to coverage from HIMSS. Each one is the same underlying pattern — take in information, reason over it, act, observe the result — pointed at a different slice of the care journey.

The economics get dramatic at enterprise scale. At Davos, executives described an AI agent ecosystem at Foxconn, built with BCG, that automates 80% of decision-making processes and has unlocked roughly $800 million in value, per CIO reporting. Fujitsu's supply chain agents cut warehousing costs by $15 million and halved staffing needs. These are agent functions doing exactly what the textbook describes: mapping streams of real-world signals into concrete decisions, thousands of times a day.

Telecommunications shows the loop at consumer scale. Telefónica's agent platform handled 4.5 million monthly calls with 90% intent accuracy — the agent perceives what a caller wants, decides how to route or resolve it, and acts, all within seconds.

Yet a tension runs through all of this. Anthropic's engineering team urges builders to "find the simplest solution possible," noting that the most successful implementations they observed used simple, composable patterns rather than complex frameworks (Building Effective Agents). Meanwhile, market analysts see multi-agent orchestration as the fastest-growing segment. The data currently sides with simplicity: single-agent systems still hold 59.2% market share, largely because they are easier to implement and audit.

That tension shapes how practitioners should think about the tools layer. An agent function is only as good as the skills and tools it can call — and most deployments still keep a human watching. A Gartner survey found 75% of organizations piloting or deploying AI agents, but only 15% considering fully autonomous ones. The loop runs; the guardrails stay on.

For teams building their own agents, the practical takeaway is that tool quality determines loop quality. A badly built skill — one that misfires, hallucinates file paths, or carries a security flaw — degrades every cycle of the agent's reasoning. That is the problem SkillzBench is being built to address: a free directory of agent skills, with sandboxed benchmarking, security scanning, and a free MCP connector so you can equip each agent with only the tools you have vetted. Before you hand an agent its tools, it is worth knowing exactly what is in the toolbox.

• Epic's Art, Penny, and Emmie show the agent loop embedded in clinical and administrative healthcare work.
• Foxconn and Fujitsu demonstrate the loop driving physical supply chain decisions, not just text generation.
• Telefónica proves the pattern holds at millions of interactions per month.
• The 59.2% single-agent market share suggests most organizations are still choosing the simpler version of the loop.

The Tools Layer: Where Agent Functions Succeed or Break

An agent function can be perfectly designed on paper and still fail in production — because it can only act through the tools it can actually reach. Anthropic's engineering team puts it plainly: agents are "just LLMs using tools based on environmental feedback in a loop," which means the reasoning layer is only half the story. The other half is what the agent can call, remember, and connect to.

That's why the industry conversation has shifted, as Precedence Research describes it, "from model intelligence to execution capability" — with memory, tool calling, retrieval, and interoperability becoming essential layers for reliable agents. Interoperability protocols are central to this shift: Anthropic's Model Context Protocol (MCP) connects agents to tools and data, while Google's A2A protocol handles agent-to-agent coordination. The scale is striking — MCP SDK downloads grew from roughly 2 million monthly at launch in late 2024 to around 97 million monthly by March 2026.

Practitioners also stress that deterministic work belongs in tools, not in the model. UiPath's guidance is to delegate math, date comparisons, and similar operations to code, because agent output isn't deterministic — retrying won't guarantee improvement. The tools layer, in other words, is where reliability is actually engineered.

But the tools layer is also where trust breaks down. Analysts note that the autonomous nature of agents creates real challenges in monitoring, auditing, and controlling decision-making processes within enterprise systems. And roughly 60% of enterprises cite non-compliance risks and data governance concerns as key adoption barriers.

The risk extends to the skills themselves. Published research has found that roughly 26% of published agent skills carry at least one vulnerability — a sobering figure when you consider that skills are the instruction packages agents increasingly rely on to get real work done.

• Discover: find relevant skills across scattered sources like GitHub, with duplicates removed
• Verify: pin every skill version to a commit SHA, then security-scan, license-check, and quality-judge it
• Benchmark: test each skill in a sandbox with the same model, with and without the skill, and measure the actual Lift
• Equip: connect chosen skills to any agent through a single free MCP server, so each agent only sees what you selected

SkillzBench, currently in pre-launch, is building exactly this — a free, independent directory and MCP connector designed to let teams discover, verify, and benchmark agent skills safely before trusting them in production. For any organization wiring agent functions into real workflows, treating the tools layer with the same rigor as the reasoning layer is quickly becoming the difference between an agent that demos well and one that delivers.

Putting It to Work: How to Think About Agent Functions in Your Business

Understanding the agent function is one thing; putting it to work in your business is where the real value lives. The good news is that the practitioners building these systems every day have converged on a handful of principles that any business can apply — no research lab required.

Start narrow and measurable. UiPath's guidance is unambiguous: agents should operate within measurable boundaries, because narrow scopes deliver consistent performance while broad prompts decrease accuracy. Instead of asking an agent to "handle customer service," give it one well-defined job — say, resolving coverage denials, the way Epic's "Penny" agent does in healthcare settings. A single-agent system doing one thing well beats a sprawling agent doing ten things inconsistently; it's no coincidence that single-agent systems held 59.2% of the market in 2025, largely because they're easier to implement.

Prefer simple, composable patterns. Anthropic's engineering team reviewed work across dozens of customer teams and found the most successful implementations used simple, composable patterns rather than complex frameworks. Their advice is to find the simplest solution possible and add complexity only when measurably needed.

Delegate deterministic work to tools. An agent function should handle the reasoning loop, not the arithmetic. UiPath notes that deterministic tasks like math and date comparisons belong in tools, not in the LLM — and since "agent output isn't deterministic, retrying won't guarantee improvement." The agent decides; the tool computes.

In practice, that leaves four rules for any deployment:

• Define a narrow, measurable scope before you build anything.
• Start with the simplest pattern that solves the problem.
• Push deterministic work into tools the agent calls.
• Evaluate the agent on task execution, not seats or licenses.

That last point matters more than it sounds. Market analysis of the agentic AI space describes a fundamental shift in how value is measured: from seats to task execution, workflow completion, and consumption. Better reasoning alone doesn't make an agent commercially valuable — what it completes does.

Of course, an agent function is only as good as the skills and tools it's given. That's why SkillzBench is building a free, independent directory of verified agent skills — every one pinned to a commit, security-scanned, and benchmarked in a sandbox — plus a free MCP connector to equip any agent with the ones you choose. Join the SkillzBench waitlist today to get free access to the verified skills directory, the MCP connector, and benchmarking when it launches. Skills, proven.