AI Agents Explained: How Autonomous Systems Are Reshaping Intelligent Work

Updated October 2026

An AI agent is software that pursues a goal on its own by planning steps and using tools. A language model decides what to do, calls search, code, or business APIs, reads the results, and repeats until the task is done. Agents differ from chatbots because the model, not a person or a fixed script, controls the next step.

This page is the glossary entry. For a deeper look at the reasoning loop, read agentic reasoning explained. For how agents change jobs and teams, see agentic reasoning in the workplace.

What is an AI agent?

An AI agent is a system that independently accomplishes tasks on your behalf. That is close to the wording in OpenAI's practical guide to building agents. The guide adds a useful test. If the model does not control workflow execution, the app is not an agent, even if it uses a language model.

Anthropic gives an even simpler picture. Agents are typically just LLMs using tools based on environmental feedback in a loop. The model acts, sees what happened, and decides again. Everything else in an agent product supports that loop.

The idea is older than large language models. The standard AI textbook, Russell and Norvig's Artificial Intelligence, A Modern Approach, devotes its second chapter to intelligent agents. It defines them by what they perceive and how they act on an environment. Language models made that old idea practical for open ended business tasks.

How is an AI agent different from a chatbot or a workflow?

The difference is control. A chatbot answers a message and stops. A workflow runs steps your engineers wrote in advance. An agent chooses its own steps and tools while it runs.

Anthropic draws the workflow line clearly. Workflows orchestrate models and tools through predefined code paths. Agents let the model direct its own process. Anthropic also warns that agents trade latency and cost for better task performance, and that autonomy brings the potential for compounding errors.

That tradeoff is why many good "agent" products are partly workflows. A support system might use fixed steps for login and billing checks, then hand an open ended question to an agent. Picking the right mix matters more than the label.

What are the parts of an AI agent?

Every agent has a model, tools, and instructions. OpenAI lists these three as the core components. Most production agents add memory, a runtime loop, and guardrails.

Anthropic calls the model plus retrieval, tools, and memory the augmented LLM, the basic building block of agentic systems. The quality of each part limits the whole agent. A strong model with vague instructions and broad permissions is still a risky agent.

How does an AI agent decide what to do?

Most agents run a reason and act loop. The ReAct paper from 2022 showed that interleaving reasoning with tool calls lets a model update its plan from real observations. On the ALFWorld benchmark it beat imitation and reinforcement learning baselines by 34 points of absolute success rate.

ReWOO, short for reasoning without observation, takes another path. It plans all tool calls up front and runs them afterward, instead of pausing to reason after each result. The authors report 5 times better token efficiency and a 4 percent accuracy gain on HotpotQA. A plan written up front is also easier for a person to review before anything runs.

Neither pattern wins everywhere. ReAct adapts better when results are surprising. ReWOO is cheaper and more predictable when the steps are clear. Planning, reflection, and memory techniques are covered in depth in agentic reasoning explained.

What are the types of AI agents?

The classic taxonomy has five types, from the intelligent agents chapter of Russell and Norvig. IBM uses the same five in its overview of AI agent types. They still help you describe what a system can and cannot do.

Language model agents blur these boxes. A coding agent plans toward a goal, weighs tradeoffs, and can store lessons in notes. In practice, teams describe agents by scope instead. A single agent handles one job end to end. A multi agent system splits work between a coordinator and specialist agents.

When does a multi agent system make sense?

Use several agents when a task splits into parallel parts that each need their own context. Anthropic's multi agent research system uses a lead agent that spawns sub agents to search in parallel. Anthropic reports such systems use about 15 times more tokens than chats, so they fit valuable, broad tasks. Tasks with tight dependencies, like most coding, fit poorly.

How do AI agents connect to tools and other agents?

Agents connect through APIs, and two open protocols now standardize the most common hops. The Model Context Protocol, or MCP, connects an agent to tools and data. Anthropic donated MCP to the Agentic AI Foundation under the Linux Foundation in December 2025.

The Agent2Agent protocol, or A2A, lets agents from different vendors find each other and delegate tasks. The Linux Foundation reported that A2A passed 150 supporting organizations in its first year. A2A also joined the Agentic AI Foundation as a hosted project in August 2026.

For a side by side view of MCP, A2A, and newer protocols, read AI agent protocols compared. For message patterns between agents, see AI agent communication.

Where are AI agents used today?

Agents work best on tasks with clear success checks and access to the right tools. Typical candidates include coding, customer support triage, research and report drafting, IT operations, and data analysis over governed tables. In each case the agent does the repetitive middle steps while a person sets the goal and reviews the outcome.

Adoption is real but uneven. McKinsey's 2026 State of AI survey found 40 percent of respondents at large organizations report scaling AI agents, up from 27 percent a year earlier. At smaller organizations the share stayed flat at about 22 percent.

Gartner expects agents to spread into ordinary software. It predicts 33 percent of enterprise software applications will include agentic AI by 2028, up from less than 1 percent in 2024. For platforms that package agents for business teams, see the enterprise AI automation platforms list.

What are the risks of AI agents?

The biggest risks are errors that compound, costs that climb, and actions taken with too much access. Gartner predicts over 40 percent of agentic AI projects will be canceled by the end of 2027. It cites rising costs, unclear business value, and weak risk controls.

Gartner also warns about agent washing, where vendors rebrand chatbots or robotic process automation as agents. It estimates only about 130 of the thousands of agentic AI vendors are real. Ask any vendor what the model decides on its own and what still runs on fixed rules.

Security is the other major concern. Every tool an agent can read from is a path for prompt injection, where hidden text steers the model. OWASP released a separate Top 10 for agentic applications in December 2025 to cover risks specific to autonomous systems.

How do you deploy AI agents safely?

Start narrow and widen access only after the agent proves itself. Anthropic advises finding the simplest solution possible and adding complexity only when needed. A single model call with retrieval often beats a full agent.

These practices cover most of the risk.

• Give each agent its own identity and the smallest set of permissions it needs.

• Require human approval before actions that spend money, delete data, or contact customers.

• Log every tool call, input, and output so you can audit and replay a run.

• Set step, time, and cost limits so a stuck loop ends on its own.

• Make agents interruptible, so a person can pause or roll back a run.

• Test against real tasks repeatedly, since agents can succeed once and fail the next time.

Governance for agents has its own design questions about policy, lineage, and enforcement at the tool layer. Those are covered in data governance for AI agents.

Why do AI agents need business context?

An agent can only act on what it understands about your business. A 2026 study found that adding explicit business semantics raised accuracy by 17 to 23 points across several frontier models. The model was the same in each case. The context changed.

That context includes metric definitions, entity models, and the rules for which data an agent may touch. Teams supply it through a semantic layer, an ontology, or a knowledge graph, often exposed over MCP. For how those options compare, read RAG vs knowledge graph vs semantic layer and the guide to context engineering for AI agents.

Which AI agent terms should you know?

These short definitions cover the vocabulary that shows up in most agent discussions.

Frequently asked questions

What is an AI agent in simple terms?

An AI agent is software that takes a goal and works toward it on its own. A language model decides which steps to take, calls tools such as search or an API, reads the results, and keeps going until the job is done or a limit stops it. OpenAI defines agents as systems that independently accomplish tasks on your behalf.

What is the difference between an AI agent and a chatbot?

A chatbot replies to a message and waits for the next one. An agent decides what to do next and acts through tools without a person directing each step. OpenAI's guide says apps that use a language model but do not let it control workflow execution, such as simple chatbots, are not agents. Many products blur the line.

What is the difference between an AI agent and a workflow?

In a workflow, your code fixes the order of steps and the model fills in each one. In an agent, the model chooses the steps and the tools at run time. Anthropic calls both agentic systems and recommends starting with the simplest option. Use a workflow when you can write the steps down ahead of time.

What are the main types of AI agents?

The classic textbook list has five types. Simple reflex agents follow rules, model based agents track state, goal based agents plan toward an objective, utility based agents weigh tradeoffs, and learning agents improve from feedback. Modern language model agents mix these traits. Teams also sort agents by scope, such as single agent or multi agent systems.

How do AI agents use tools?

The model emits a structured tool call with a name and arguments. Your runtime executes it, such as a database query or an API request, and returns the result to the model. The Model Context Protocol, now hosted by the Agentic AI Foundation under the Linux Foundation, gives agents one standard way to discover and call tools.

Are AI agents replacing human workers?

Not on their own. Gartner predicts at least 15 percent of day to day work decisions will be made autonomously by agentic AI by 2028. That still leaves people setting goals, approving risky actions, and checking results. Most teams use agents for narrow, repetitive tasks and keep humans in charge of judgment calls and exceptions.

How many companies use AI agents in 2026?

Adoption is growing but uneven. McKinsey's 2026 State of AI survey found 40 percent of respondents at large organizations report scaling AI agents, up from 27 percent a year earlier. At smaller organizations the share stayed flat at about 22 percent. Scaling agents is still far from universal, especially outside the largest firms.

What are the biggest risks of AI agents?

The main risks are compounding errors over many steps, rising cost, prompt injection through tool inputs, and agents taking actions with too much access. Gartner expects over 40 percent of agentic AI projects to be canceled by the end of 2027. OWASP published a dedicated Top 10 for agentic applications in December 2025 to cover these threats.

What is agent washing?

Agent washing is when vendors rebrand chatbots, assistants, or robotic process automation tools as agentic AI without real agent capabilities. Gartner raised the concern in 2025 and estimated that only about 130 of the thousands of vendors claiming agentic AI are real. Ask any vendor what the model actually decides on its own.

Why do AI agents need business context?

An agent can only act on what it understands about your business. A 2026 study found that adding explicit business semantics raised accuracy by 17 to 23 points across frontier models. Shared metric definitions, entity models, and a semantic layer give agents the meaning they need to query and act correctly inside a company.