AI agent

What is an AI agent?

An AI agent is an AI system that doesn't just produce a single answer but autonomously runs a series of steps to reach a goal. It uses a language model as its brain, plans what should happen, calls tools to take actions, and adjusts its approach based on the results.

Where a classic chatbot replies to one question at a time, an agent can break down a problem, look up data in two systems, run a calculation, draft an email, and only then come back with a proposal. All without you typing every step in between.

The term agentic AI became a buzzword in 2024 and 2025, but the underlying ideas are older. What's new is that modern language models are finally good enough at reasoning and using tools to make this approach viable in production.

Plain chatbot versus AI agent

The difference is in autonomy and in how many steps the system takes.

Plain chatbot
You ask a question, the model replies. Everything it knows must fit into that one response. No memory between questions, no access to live data unless you add it yourself with RAG.

RAG chatbot
Same as above, but with a fixed step before generation: search a vector database, then answer. The flow is hard-coded.

AI agent
The agent decides for itself which steps are needed. It can search multiple times, combine tools, evaluate intermediate results, and adjust its plan. The flow is dynamic and depends on what the user asks and on what intermediate results look like.

How does an AI agent work?

Under the hood an agent runs a loop that usually has three parts: think, act, and observe.

1. Think
   The language model looks at the goal and picks the next step. This can be an internal thought ("I need the customer number first") or a direct action ("call tool X").

2. Act
   The agent calls a tool. This could be an API, a database query, a search, a calculation, or another AI service. In practice this is usually referred to as function calling or tool calling.

3. Observe
   The tool's result comes back to the model, which decides again: done, or another step needed? If another step, which one?

This loop is often described as ReAct (Reasoning + Acting) or Plan-and-Execute in the literature. Variants differ mostly in how explicit the plan is, and in how far the model is allowed to override itself.

On top of the model itself, every agent has three extra building blocks:

• Tools
  Functions with a clear description, inputs, and outputs. The model picks which tool to use based on the description.

• Memory
  Short-term (the current conversation) and long-term (what was said or inferred earlier). Without memory an agent loses context fast.

• Orchestrator
  The framework that runs the loop, enforces limits (step count, cost, which tools are safe), and handles failures.

When do you use an AI agent?

1. Multi-step tasks with decision points
   Ticket triage, invoice processing, lead qualification. The agent looks at context, asks follow-up questions where needed, and routes or decides within clear boundaries.

2. Assistants for knowledge workers
   A sales agent that drafts quotes, an HR agent that looks up policy and pre-fills forms, a BI agent that writes DAX queries based on business questions.

3. Automation with variation
   Where a fixed workflow engine would break because every case differs slightly, an agent can adjust the path per case.

4. Research and synthesis
   An agent that searches multiple sources, summarises them, and flags contradictions. Think competitive analysis or legal research.

5. Data analysis in natural language
   Ask in plain English, the agent translates into a query, runs it, interprets the result, and suggests follow-up questions.

Risks and limitations

An agent amplifies an LLM's capabilities, and also its risks.

Infinite loops
Without a cap on steps or time, an agent can keep looping on itself. Always set hard limits on iterations and cost.

Wrong tool use
When tool descriptions are vague, the model sometimes picks the wrong one. Clear names, examples, and strict input validation reduce this risk.

Irreversible actions
An agent that can pay an invoice or send an email directly can't undo its mistakes. For those actions always build in human-in-the-loop: the agent prepares, a human approves.

Prompt injection and jailbreaking
Malicious input can instruct the agent to do something other than intended. The more tools an agent has, the larger the attack surface. Treat input from external sources (emails, websites, documents) with suspicion.

Cost and latency
Every step costs an LLM call. An agent that runs ten steps is ten times more expensive and slower than a simple RAG call. Decide upfront whether the value justifies the cost.

Platforms for AI agents

Several platforms let you build agents without writing everything from scratch.

• Microsoft Copilot Studio
  Low-code environment to build agents with access to Microsoft 365 data and Power Platform connectors. A strong option for organisations already in the Microsoft ecosystem.

• Azure AI Foundry
  More technical, for building agents on Azure OpenAI models with custom tools and vector search.

• LangGraph, CrewAI, and AutoGen
  Open-source frameworks for building agents and multi-agent systems in Python. Flexible, but requires engineering work.

• OpenAI Agents SDK, Anthropic's MCP, and Claude Skills
  Building blocks from the model vendors themselves. A strong fit for teams that want to stay close to one model family.

The choice comes down to who will build and maintain the agent, and which existing data and tools you want to integrate. For a first project in a Microsoft environment, Copilot Studio is usually the fastest path to a working prototype.