What is an AI agent in simple terms?

An AI agent is software that gets a job done for you. You give an AI agent a goal, and it works out the steps, uses the systems it is connected to, checks its own work, and asks a person only when it needs to.

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

A chatbot answers questions in a conversation. An AI agent takes actions in other systems to complete a task. A chatbot tells you how to reschedule a meeting. An AI agent reschedules it, notifies the attendees, and updates the record.

What is the difference between an AI agent and agentic AI?

Agentic AI is the approach: AI that acts toward goals with limited supervision. An AI agent is one software worker that applies that approach to a specific job. Agentic AI systems usually coordinate several AI agents.

Do AI agents need a large language model?

Most modern AI agents use a large language model as their reasoning engine. The model alone is not the agent. An AI agent combines the model with tools, memory, instructions, and guardrails so it can act, not just generate text.

Are AI agents safe to use in business?

AI agents are safe when they run with least-privilege access, human approval for sensitive actions, and full audit logs. NIST launched an AI Agent Standards Initiative in February 2026 to develop shared practices for agent security, identity, and interoperability.

What is agent washing?

Agent washing is the rebranding of existing chatbots, assistants, or automation tools as AI agents without real agentic capability. To test a claim, ask for a demo in which the AI agent completes a multi-step task across your own systems.

How do you get started with AI agents?

Start with one high-volume, low-risk workflow, such as post-meeting follow-ups. Connect the data the AI agent needs, set permissions and approval rules, and measure time saved and data quality for 30 to 60 days before expanding.

What Is an AI Agent? Definition, Types & Examples

An AI agent is software that can take a goal and act on it. It is the building block of agentic AI, and it is fast becoming a standard part of business software.

Gartner projects that by 2028 a third of enterprise software applications will include agentic AI, up from under 1% in 2024. In sales, 54% of sellers say they have already used agents, according to Salesforce's 2026 State of Sales research.

This guide explains what an AI agent is, how it works, the main types, and where agents already do useful work.

What Is an AI Agent?

An AI agent is a software program that uses artificial intelligence to perceive its context, make decisions, and complete tasks on a user's behalf. An AI agent plans the steps toward a goal, calls tools such as APIs, email, or a CRM, checks the result, and continues with limited human direction.

The difference from earlier AI tools is action. A chatbot can tell you how to update a record. An AI agent updates it. A language model can draft an email. An AI agent decides the email is needed, drafts it, and queues it for approval.

AI agents are the workers inside agentic AI systems. Agentic AI is the approach. An AI agent is one worker that applies it to a specific job.

AI Agent vs. Chatbot vs. Copilot

	Chatbot	Copilot	AI agent
Starts with	A user message	A user request inside an app	A goal or an event
What it does	Answers questions	Suggests and drafts for approval	Plans and completes multi-step tasks
Uses tools	Rarely	Within one app	Across connected systems
Human role	Asks every question	Approves every step	Sets goals and guardrails, reviews results

Types of AI Agents

Task agents complete one job, such as updating CRM records after a call.
Conversational agents talk to customers or employees and take actions during the conversation.
Orchestrator agents coordinate other agents and pass context between them.
Autonomous agents run with little oversight inside set limits.

How Does an AI Agent Work?

An AI agent works in a loop: sense, think, act, check, and remember.

Receive a goal or trigger. A person gives an instruction, or an event occurs, such as a meeting ending.
Perceive. The agent reads the data it needs from connected systems: CRM records, emails, calendars, transcripts, and documents.
Reason and plan. A large language model interprets the goal and breaks it into ordered steps.
Act. The agent calls tools to do the work, such as writing to a record, drafting a message, or creating a task.
Observe and correct. The agent checks the outcome, retries on failure, and escalates to a person when confidence is low or the action is sensitive.
Remember. The agent stores context so later steps and later runs improve.

Guardrails surround the loop. Permissions limit what the agent can touch, approvals gate high-impact actions, and logs record every step.

Key Features of an AI Agent

Goal orientation. An AI agent works toward an outcome, not a single prompt.
Tool use. An AI agent takes real actions through APIs and connectors.
Memory. Short-term memory tracks the task. Long-term memory carries account and user context.
Planning and self-correction. An AI agent re-plans when a step fails or conditions change.
Permissions and approvals. Access is scoped, and sensitive actions wait for a person.
Audit logs. Every action can be reviewed.
Collaboration. An AI agent can hand work to other agents through an orchestration layer.

Benefits of an AI Agent

Multi-step work gets finished. The agent completes the task instead of describing it.
Always available. Agents work at any hour and absorb volume spikes.
Consistent execution. The same standard applies every time.
Scale without linear headcount. Capacity grows without hiring for every increase in volume.
People focus on judgment. Staff spend time on relationships and decisions.

The gains sellers expect are concrete: Salesforce found they expect agents to cut prospect research time by 34% and email drafting time by 36%.

Results are not automatic. Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027 because of rising costs, unclear business value, or inadequate risk controls. Gartner also warns of agent washing and estimates that only about 130 vendors offer genuine agentic AI.

AI Agent Use Cases

Customer service. Agents resolve routine tickets from start to finish.
Software engineering. Coding agents write, test, and fix code.
IT operations. Agents triage and remediate common incidents.
Research. Agents gather sources and produce cited summaries.
Sales. Agents qualify inbound leads, prepare meetings, draft follow-ups, update the CRM, and coach reps. SeeAI Sales Agents Explained.

How ASPR fits. ASPR is built from specialized AI agents for sales, including Pre-Meeting, Post-Meeting, CRM, Coaching, Document, and Sales Knowledge agents. They share context and run as an execution layer inside the CRM. Explore ASPR's AI agents.

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Frequently Asked Questions About AI Agents

Related glossary terms:Agentic AI, Autonomous AI Agent, Multi-Agent AI,AI Agent Orchestration,AI Sales Agent, AI Agentic Workflow
Existing pages:AI Sales Agents Explained,AI Agent Orchestration,Agentic AI at Scale,ASPR AI Agents

Sources

Gartner: Over 40% of Agentic AI Projects Will Be Canceled by End of 2027, June 2025
Salesforce: State of Sales 2026 announcement, survey of 4,050 sales professionals
NIST: AI Agent Standards Initiative, February 2026