Agentic AI is software that uses AI models, usually large language models, to work toward a goal by planning steps, calling tools and other systems, checking results and deciding what to do next, with limited human direction. Where a chatbot answers a question, an AI agent is meant to complete a task, such as resolving a support request or preparing a report from several sources. The “agent” here is software, not a human agent in a contact center.
At a glance
- An AI agent is given a goal, then plans and takes steps toward it, often across several systems.
- Agents act through tools you connect: APIs, databases, business applications, email or a browser.
- How much an agent does on its own varies by product and configuration, from suggesting actions for approval to completing tasks unattended.
- Because agents take actions, errors and manipulation can change data or send messages, so permissions, approvals and logging matter more than with a chatbot.
- Accountability for what an agent does generally stays with your organization.
What problem it solves
A lot of office work is not one question and one answer. It is a chain: read a request, look something up in one system, check a policy in another, update a record, notify someone. Traditional automation such as robotic process automation (RPA) handles chains that rarely change, but it struggles when inputs vary or require judgment. Chat-based generative AI can draft and summarize, but on its own it doesn’t do the work in your systems.
Agentic AI tries to close that gap: software that can interpret a varied request and carry it through several steps. Typical early uses are customer service requests, IT help desk tickets, sales and back-office research, and internal reporting. The appeal for a mid-market company is doing more with the same headcount; the risk is software taking real actions in real systems.
How it works
A model that plans. At the center is usually a large language model (LLM). Given a goal and instructions, it breaks the goal into steps and decides which tool to use next.
Tools and connections. The agent can only act through tools it has been given: an API to the CRM, read access to a knowledge base, the ability to create a ticket. Standards such as the Model Context Protocol (MCP) are one way to connect agents to tools and data.
A loop. The agent takes an action, reads the result, and decides whether it is done, needs another step or should hand off to a person. Some products run several agents that pass work between them, an arrangement often called a multi-agent system, where specialized agents handle different steps and one may coordinate the others.
Guardrails. Well-run deployments define what each agent may read and change, which actions need human approval, spending or volume limits, and full logs of every step. These controls are set by the buyer and the product, not by the model.
When it matters for buyers
- When the board asks for an AI plan. Agents are often pitched as the next step after chat tools; a clear, narrow first use case beats a broad mandate.
- When evaluating contact center and service platforms. Many contact center and help desk products include agent features; compare what they can actually do unassisted and how they hand off to people.
- When granting system access. An agent with broad credentials is a new kind of privileged account. Treat it like one.
- When setting AI governance. Agents raise questions of ownership, approval and audit that chat tools do not.
- When security reviews AI. Agents that read emails, web pages or documents can be targeted by prompt injection, where hidden instructions in content try to redirect them.
For a wider view of AI options and how to evaluate them, see our artificial intelligence overview.
Questions to ask vendors
- What actions can the agent take on its own, and which require a person to approve?
- Which systems and data does it need access to, and can we restrict it to least privilege?
- How are the agent’s steps logged, and can we review and export those logs?
- How do you protect against prompt injection from emails, documents or web pages it reads?
- What happens when the agent is unsure or fails: does it stop, retry or hand off to a person?
- How is it priced: per task, per conversation, per user or by usage, and what limits stop runaway costs?
- Which AI models does it use, and is our data used to train them?
- What liability do you accept if the agent takes a wrong action?
How it differs from generative AI
Generative AI is the broad category of models that produce text, images, code or audio. Agentic AI uses those models but adds planning, tool use and actions: it is about doing a task, not only producing content. An AI assistant sits in between; it usually helps a person who stays in control, while an agent is designed to complete steps with less supervision. Conversational AI describes systems that talk with people; an agent may or may not have a conversational front end.