What Are AI Agents? A Practical Explainer With Real Business Examples

What are AI agents? Systems that plan, use tools, and complete tasks on their own. How they work, the five types, and real business examples.

By Galactic News Editorial · Published 2026-11-06T22:46:51+00:00 · Updated 2026-11-06T22:46:51+00:00

An AI agent is a system that uses a large language model to pursue a goal on its own. It plans the steps, calls tools, checks the results, and adjusts as it goes. A chatbot answers questions. An agent completes tasks. This explainer covers what AI agents are, how they work, the main types, and where businesses actually use them today.

What are AI agents, in plain terms?

Anthropic, which builds the Claude models, draws a useful line. It groups these systems under one label, agentic systems, and splits them in two. Workflows are systems where models and tools follow predefined code paths. Agents are systems where the model dynamically directs its own process and tool usage. The model, not the developer, decides the next step.

The building block is an augmented LLM. That means a language model equipped with retrieval, tools, and memory. It can search for information, call software functions, and remember what matters. An agent then runs that model in a loop. It starts from a command or a conversation. It plans, acts, and collects ground truth from the environment at each step, such as tool results or code output. It can pause for human feedback at checkpoints. It stops when the task is done, or when a stopping condition, such as a maximum number of iterations, is reached.

Agents vs workflows vs chatbots

The three categories blur in marketing copy, so use behaviour as the test.

• Chatbot: it responds. You ask, it answers. It does not pursue a goal across multiple steps or systems.
• Workflow: it follows a path you designed. The model fills in each step, but the sequence is fixed in advance.
• Agent: it decides the path. Given a goal, it breaks the work down, picks tools, and reacts to what it finds.

Google Cloud's Office of the CTO makes the same point from the business side. Agents act as representatives performing actions for your business. They remove friction from processes instead of only answering questions about them.

The five types of AI agents

IBM classifies agents by how they make decisions, from simplest to most advanced. The categories come from classic AI research, and modern LLM agents sit at the complex end.

• Simple reflex agents follow condition-action rules. A thermostat is the standard example: if the temperature drops below a threshold, turn on the heat. They keep no memory of the past.
• Model-based reflex agents also keep an internal model of the world. That model lets them track state and handle situations where not everything is visible.
• Goal-based agents work toward a specific objective. They evaluate possible actions and pick the one most likely to reach the goal.
• Utility-based agents weigh trade-offs. A self-driving car, for example, balances speed, fuel efficiency, and safety instead of chasing one goal.
• Learning agents improve from feedback over time. They combine a performance element, a learning element, a critic, and a problem generator.

All five types can also work together. IBM describes multi-agent systems where each agent specialises in the part of a task it handles best. Higher-level agents hold the overall goal. Lower-level agents execute specific subtasks.

Real business examples

Customer support. Anthropic calls support a natural fit for open-ended agents. Conversations flow naturally, and the agent can pull customer data, order history, and knowledge base articles through tools. Actions such as issuing refunds or updating tickets can be handled programmatically. Success is easy to measure through resolved cases. Anthropic also notes that several companies charge only for successful resolutions, a pricing model that shows confidence in the approach.

Coding. Anthropic's own coding agent resolves tasks from the SWE-bench benchmark. Those tasks involve edits across many files, based only on a task description. Coding suits agents because the subtasks cannot be predicted in advance, and the environment gives clear feedback through tests.

Multi-agent teams. Google Cloud describes specialised agents working like a human team. One agent analyses data. Another writes the report. A third validates the output. Breaking a complex problem into subtasks lets each agent be tested and improved on its own.

Everyday assistance. Google Cloud also lists advanced virtual assistants that carry out multi-step requests. Its example: find restaurants near an event, check reviews, and book one. That is research, judgement, and action in a single request.

Five workflow patterns behind most agent systems

Anthropic catalogues five recurring workflow patterns. Most production systems combine several of them.

• Prompt chaining splits a task into fixed steps. Each model call processes the output of the previous one. One example: write marketing copy, then translate it.
• Routing classifies an input and sends it to a specialised follow-up. Customer service queries, for example, split into general questions, refund requests, and technical support.
• Parallelization runs calls at the same time. Subtasks run side by side, or the same task runs several times and the outputs are compared for confidence.
• Orchestrator-workers adds a central model that breaks a task down, delegates pieces to worker models, and merges the results. The subtasks are decided at run time, not in advance.
• Evaluator-optimizer pairs a generator with a critic. One call produces output, another scores it against clear criteria, and the loop repeats.

Full agents sit beyond these patterns. They keep control of the loop itself, rather than following a path a developer drew in advance.

When you should not use an agent

Anthropic's core advice is restraint. Find the simplest solution that works, and add complexity only when it demonstrably improves outcomes. Agentic systems trade latency and cost for better task performance. For many applications, a single well-designed model call with retrieval is enough. Agents also carry a specific risk: errors compound across steps. Anthropic recommends extensive testing in sandboxed environments, with appropriate guardrails.

Google Cloud's practitioners give matching advice. Define why you need the agent. Start small and be specific. Keep a human in the loop. Understand your data before you automate with it. If a fixed workflow solves your problem, ship the workflow. Reach for an agent when the steps cannot be known in advance and the result is worth the extra cost and oversight. Anthropic summarises its own approach in three principles: maintain simplicity in the design, prioritise transparency by showing the agent's planning steps, and craft the tool interface as carefully as the prompts.