Skip to main content
AI & Agents

AI Agent

An AI agent is a software system that pursues a goal by taking actions, not just producing text. It reasons about what needs to happen, calls external tools such as APIs and databases, reads the results, and decides its next step — repeating until the task is complete or it escalates to a human.

The distinction that matters is between producing output and producing change. A chatbot given the same question returns a paragraph explaining a refund policy. An agent checks the order, applies the policy, issues the refund, updates the CRM and closes the ticket. The presence of a language model is not what makes something an agent — the ability to act on external systems is.

That capability is also what makes agents harder to build. When a chatbot is wrong, a user reads a bad paragraph. When an agent is wrong, money moves or records change. Production agents therefore carry machinery chatbots do not need: permission-scoped tools, spend and iteration limits enforced outside the model, approval gates on irreversible actions, and complete traces of every decision.

Goal-directed

Given an objective rather than a single question, and decides its own steps toward it.

Tool-using

Calls APIs, databases, email and internal services — the mechanism by which it changes state.

Iterative

Loops: act, observe the result, decide again, while tracking what it has already tried.

Bounded

Constrained by limits enforced server-side, with an explicit path to escalate when uncertain.

Codazz builds this in production — AI Agent Development.

FAQ

AI Agent
FAQ.

Common questions about ai agent.

Ask Us Anything

A chatbot returns text; an AI agent takes action. A chatbot can tell a customer what the refund policy says. An agent checks the order, issues the refund, updates the record and closes the ticket. If the system cannot change anything outside the conversation, it is a chatbot regardless of how capable the underlying model is.

For well-scoped tasks with proper guardrails, yes — thousands of businesses run them today. Reliability comes from engineering rather than the model: confidence thresholds with an escalation path, hard limits enforced outside the model, human approval on irreversible actions, and a shadow-mode period before anything runs autonomously. Without those, agents are not production-ready whichever model powers them.

Most knowledge work that involves reasoning over information and then acting on it: support ticket resolution, invoice processing, data entry and enrichment, report generation, email triage, lead qualification, contract review, and multi-system reconciliation. The practical test is whether the task has a checkable definition of done and touches systems that expose an API.

Related terms

Agentic AI

Agentic AI is the general term for AI systems that plan and act autonomously toward a goal rather than responding to one prompt at a time.

Multi-Agent System

A multi-agent system is an architecture where several specialised AI agents work on one goal, typically coordinated by a supervisor agent that decomposes the task, routes each part to the right specialist, and checks results before accepting them.

Tool Calling

Tool calling is the mechanism that lets a language model invoke external functions — querying a database, calling an API, sending an email.

Human-in-the-Loop (HITL)

Human-in-the-loop is a design pattern where an AI system pauses and requires a person to approve, modify or reject an action before it proceeds.

Browse the full glossary →