How AI agents work: a plain-English guide for Australian businesses

An AI agent is an AI system that can take actions, not just answer questions. It uses a language model to reason, breaks a goal into steps, calls external tools (search, APIs, databases, code execution), evaluates the results, and keeps going until the task is done or it needs help. The difference between a chatbot and an agent is autonomy across multiple steps.

The anatomy of an AI agent

• →Reasoning model: the LLM (Claude, GPT-4o, etc.) that decides what to do next
• →Tools: functions the agent can call, APIs, database queries, web search, code execution
• →Memory: short-term context window plus optional long-term retrieval via RAG
• →Orchestrator: the loop that runs the model, handles tool calls, and manages state
• →Guardrails: constraints, cost limits, and human approval gates for risky actions

How AI agents work step by step

The agent receives a goal. It reasons about what needs to happen first and calls a tool (e.g. a database query). It reads the result, decides what to do next, and either calls another tool or produces an output. This loop runs until the goal is complete or the agent hits a guardrail and pauses for human review. The whole cycle can take seconds for simple tasks or minutes for complex multi-step workflows.

Real applications for Australian businesses

In finance: agents that monitor regulatory filings and summarise changes relevant to your business. In mining and resources: agents that process sensor data, flag anomalies, and draft incident reports. In retail: agents that monitor competitor pricing, draft promotional responses, and update internal dashboards. In legal: agents that process contracts, extract key clauses, and flag non-standard terms for review.

Testing agents before you trust them

Agents fail differently from regular software. They can reason their way into unexpected states, misinterpret tool outputs, or take irreversible actions based on bad data. Every production agent we ship at JDML has an evaluation harness with known good and bad test cases, explicit guardrails on destructive actions, full trace logging, and human review queues for anything high-stakes. That's not optional, it's what production means.