An AI agent for business is software that uses a language model to work toward a goal on its own: it decides which steps to take, uses tools such as your task list, calendar or documents, checks the results and continues until the job is done or a person needs to decide. A chatbot answers a question; an agent does a piece of work. For teams, the most useful agents today handle well-defined coordination work, such as follow-ups, routing requests and preparing briefings, within limits a person sets.

A working definition

The clearest definition comes from Anthropic's engineering guide on agents (Anthropic, 2024). It separates two kinds of AI systems:

Workflows: the model and tools follow steps that are fixed in code. Example: every new support email is summarized and filed in the same way.
Agents: the model directs its own process and tool use, deciding how to reach the goal. Example: "Find out why this project is late and tell the right person."

OpenAI's developer documentation describes agents in similar terms: systems that can plan and complete tasks using tools, work with other agents and keep context across steps (OpenAI, 2026).

In practice, an agent has four parts:

A model that reasons about what to do next.
Tools it can use: read tasks, send a message, create a document, search knowledge.
Context and memory: what it knows about your company, your goals and earlier steps.
Guardrails: permissions, stopping rules and points where a person must confirm.

Agent, assistant, automation: what's the difference?

	Automation	AI assistant (chatbot)	AI agent
Who decides the steps	You, in advance	You, one prompt at a time	The agent, within limits
Uses tools	Fixed actions	Sometimes	Yes, chooses which
Works without being asked	Yes, on a trigger	No	Yes, toward a goal
Handles unexpected cases	No	Partly	Yes, or escalates
Example	"When status = Done, notify the lead"	"Summarize this thread"	"Make sure every task this week has an owner"

Gartner warns about "agent washing": vendors relabeling assistants, robotic process automation and chatbots as agents without real agentic capability. It estimates that only about 130 of the thousands of agentic AI vendors are real (Gartner, 2025). The table above is a quick test.

Examples for teams

These are realistic uses for a team of 5 to 100 people today:

Follow-up agent. Notices a task is late or blocked and asks the owner for an update, or brings it to the lead. See AI tool that follows up on tasks.
Assignment agent. Offers unassigned work to the person with the right skills and capacity.
Briefing agent. Writes a lead's morning summary: what moved, who is waiting, what is due, which decision is open.
Knowledge agent. Answers "How do we price a website project?" from the company's own pages, with the source.
Intake agent. Reads incoming requests, classifies them and creates tasks in the right project.
Planning agent. Drafts a project plan with phases, tasks, owners and dependencies from a short description, for a person to review. See How to plan a project with AI.
Meeting agent. Turns action items from a meeting into tasks with owners.

What these have in common: the goal is clear, the agent can check its own result against real data (tasks, dates, people), and a person stays in charge of decisions.

How widely are businesses using agents?

Adoption is growing, but unevenly:

In McKinsey's 2026 global survey, 40 percent of respondents from organizations with more than $1 billion in annual revenue report scaling AI agents, up from 27 percent a year earlier. Among smaller organizations, the share stayed flat at 22 percent (McKinsey, 2026).
In Microsoft's 2025 Work Trend Index, 81% of leaders expected agents to be moderately or extensively integrated into their company's AI strategy within 12 to 18 months (Microsoft, 2025).
Gartner predicts that at least 15% of day-to-day work decisions will be made autonomously through agentic AI by 2028, up from 0% in 2024 (Gartner, 2025).

There is also a warning. Gartner expects over 40% of agentic AI projects to be canceled by the end of 2027 because of escalating costs, unclear business value or inadequate risk controls (Gartner, 2025).

What makes an agent work in practice

Anthropic's guidance and the failure reasons Gartner names point to the same rules:

Start with the simplest thing that works. Many tasks need a single model call or a fixed workflow, not an agent. Agents trade speed and cost for flexibility (Anthropic, 2024).
Give it real context. An agent that cannot see your tasks, deadlines and knowledge will guess. Most failures are context failures. See What is a company brain?
Keep a person in the loop for decisions. Use checkpoints where a person confirms, and clear stopping conditions (Anthropic, 2024).
Make actions visible. Every action the agent takes should be visible and reversible.
Measure one business outcome. For example, hours a lead spends collecting updates, or the number of tasks without an owner.

Common risks

Wrong actions with confidence. Restrict what the agent may do, and check outputs against real data.
Noise. An agent that sends too many messages gets ignored. Limit how often it may interrupt people.
Cost. Agents make many model calls. Prefer narrow tasks over open-ended ones.
Access. An agent should only see what the person it acts for may see.

How Orakis does it

Orakis has a built-in agent called Alfred and a background coordinator called the Company Brain. Alfred knows the workspace's goals, tasks, documents and knowledge, answers questions and creates or updates work. The Company Brain notices what needs attention and brings it to the right person. Which follow-up goes to whom is decided by fixed, tested rules; a model is only asked who fits an unassigned task and whether a new task depends on another, and its answers are restricted in code to real people and real tasks. Leads decide how much Orakis may approve on its own, and every approval is visible.

FAQ

Is ChatGPT an AI agent? In a normal chat, ChatGPT is an assistant: it answers your prompt. When it uses tools to complete a multi-step task on its own, it acts as an agent.

Do AI agents replace employees? Today they mostly take over coordination and routine steps. Decisions about priorities, people and trade-offs still need a person.

How do I connect an agent to my company's tools? Often through the Model Context Protocol (MCP), an open standard for connecting AI applications to data and tools. See What is MCP?

What Is an AI Agent for Business? Examples for Teams