Glossary
What is AI agent?
Last updated: 2026-05-05
Definition
An AI agent is a software program that uses a large language model (LLM) to autonomously plan and complete a task, combining reasoning, tool use, and memory. Unlike a one-shot prompt, an agent can break a goal into steps, call external tools or APIs, and decide what to do next based on intermediate results.
Why AI agent matters
AI agents move generative AI from "answers in chat" to "work that gets done." Gartner forecasts that by 2028 a third of enterprise software applications will include agentic AI, up from less than 1% in 2024 — making the agent the new unit of automation alongside the function and the workflow.
How AI agent works
- 1A user gives the agent a goal (e.g. "research the top 5 EU AI Act compliance requirements for SaaS platforms").
- 2The agent plans the steps needed to achieve the goal — typically using an LLM to break the task down.
- 3For each step the agent calls a tool: search, retrieve documents, run code, send an email, query a database.
- 4The agent observes the result of each tool call and updates its plan.
- 5The agent stops when it reaches the goal or hits a stopping condition (max steps, human approval needed, error).
- 6The agent returns a final answer or hands off to a human reviewer.
Examples
- A sales-research agent that takes a company name, scrapes the company website, queries Crunchbase, and returns a one-page brief.
- A support-triage agent that reads an inbound email, classifies the issue, drafts a reply, and routes high-risk tickets to a human.
- A compliance-review agent that scans a document for PII before it is sent to a third-party LLM.
References
Related concepts
AI agent platform
An AI agent platform is software that lets organizations build, deploy, govern, and monitor AI agents at scale — typically with a workspace UI, multi-LLM access, knowledge bases, integrations, scheduling, and audit logging. The platform replaces the need for each team to assemble agent infrastructure from raw frameworks.
Multi-agent orchestration
Multi-agent orchestration is the practice of chaining multiple specialized AI agents into a single workflow, where each agent has a defined role (researcher, writer, reviewer, publisher) and outputs flow from one agent to the next. The orchestrator decides the order, handles retries, and enforces guardrails between steps.
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is a technique that grounds a large language model in a specific corpus of documents at query time. Instead of relying only on what the model learned during training, RAG retrieves relevant passages from your data and adds them to the prompt — letting the model answer using your knowledge, current and proprietary.
Human-in-the-loop (HITL)
Human-in-the-loop (HITL) is a design pattern where a human reviewer must approve, edit, or veto an AI agent's output before it executes a consequential action. The agent pauses, surfaces what it is about to do, waits for the human, and then proceeds — a deliberate brake to keep autonomy bounded.
FAQ
AI agent — common questions
- What is the difference between an AI agent and an AI assistant?
- An AI assistant typically responds to a single prompt with a single answer. An AI agent breaks a goal into multiple steps, calls tools, and decides on its own what to do next based on intermediate results. Assistants reply; agents act.
- Are AI agents the same as chatbots?
- No. Chatbots are typically conversational interfaces with limited actions. AI agents can be conversational but their defining trait is autonomous tool use — they read documents, query databases, send messages, and chain those actions to complete goals without per-step human prompting.
- Do AI agents replace workflow automation tools?
- Not entirely. Workflow automation (Zapier, n8n, Make.com) excels at deterministic plumbing. AI agents excel at fuzzy, judgement-heavy work (research, drafting, classification). The two are complementary: agents call workflow tools, and workflow tools trigger agent runs.
- Are AI agents compliant with the EU AI Act?
- Compliance depends on the deployment, not the technology. Under the EU AI Act, AI agents inherit the risk classification of their use case. AgentWorks ships per-agent risk classification, audit logging, PII redaction, and human-in-the-loop controls so agents meet EU AI Act obligations out of the box.