AI agent – what it is, how it works and what you need to know

Definition

An AI agent is an AI system that can plan, make decisions and carry out multi-step actions on its own to reach a goal – without someone steering it at every step. Unlike a chatbot that answers one question at a time, an agent can take on complex tasks.

What are AI agents?

An AI agent is a system in which a large language model (LLM) acts as the central decision-maker, combined with the ability to use tools, memory and an iterative problem-solving loop. The agent is given a goal, breaks it down into subtasks, decides for itself which actions are needed and carries them out – reading data, calling APIs, writing and running code, updating systems – until the goal is reached.

This sets it fundamentally apart from an ordinary LLM. Ask ChatGPT a question and you get an answer. Ask an agent and you set a workflow in motion. The agent is not reactive – it is goal-directed.

How an AI agent works – the cognitive loop

At the core of an AI agent is a cognitive loop with four steps that repeat until the task is solved:

1. Perceive – The agent takes in information: the user's goal, the available tools, data from external systems and context from memory.
2. Plan – The LLM reasons and breaks the goal down into concrete substeps. What is needed first? Which tools can solve each subtask?
3. Act – The agent carries out an action: searches the web, reads a file, calls an API, writes code, sends a message.
4. Reflect – The agent evaluates the result of the action. Did it work? Does it need correcting? How does it affect the next step?

This loop is what separates agents from simpler AI systems. The agent is not limited to a single round trip – it can iterate, backtrack and try again until the goal is reached.

The components of an agent

A fully functioning AI agent has a few essential parts:

• An LLM as the brain – The language model does the reasoning and planning. Modern frontier models such as Claude, GPT and Gemini are designed for exactly this kind of agentic use, with long-running reasoning, tool use and the ability to handle complex instructions.
• Tools – The agent has to be able to act on the world. Tools might include web search, code execution, database access, sending email or calling external APIs, and are standardised through protocols such as MCP.
• Memory – Short-term memory (context in the current session), long-term memory (vector databases that store earlier interactions and relevant information) and episodic memory (knowledge of tasks it has carried out before).
• Orchestration logic – The code that runs the loop, handles errors, escalates to a human when something cannot be resolved and coordinates multiple agents.

From single agents to multi-agent systems

Complex goals often call for specialisation. Instead of one generalist agent doing everything, you build a multi-agent system in which different agents have different roles:

• An orchestration agent receives the user's goal, splits it up and delegates to specialist agents
• Specialist agents are each responsible for a narrow domain: search, coding, analysis, communication
• The agents report back, and the orchestration agent combines the results

This pattern is already in use at scale. Amazon has built thousands of agents internally since 2025. JPMorgan Chase uses agents for fraud detection and loan processing. Walmart has LLM-powered agents for customer service and merchandise planning.

What AI agents need from humans

Despite their autonomy, AI agents are not self-sufficient. Three things decide whether an agent actually delivers value:

• Clear goals – An agent is only as good as the task it is given. Vague instructions produce vague results. A human has to define what "done" means.
• The right tools and permissions – An agent without access to the right tools cannot complete the task. Granting too many permissions, on the other hand, creates security risks.
• Human control when the stakes are high – Actions with irreversible consequences – deleting data, publishing externally, carrying out transactions – need human approval. Human-in-the-loop design is not a limit on what the agent can do but a necessary safety valve.

Use cases in 2026

The range is wide. Today, agents are used for everything from writing code to customer service, and from fraud detection to marketing automation. Some concrete examples:

• Software development: Agents that can read code, write tests, fix bugs and open pull requests
• Sales and CRM: Agents that monitor leads, summarise meetings and update the CRM without manual input
• Content production: Agents that do keyword research, write drafts, publish and report – with human review built in at strategic points
• IT ops: Agents that monitor systems, diagnose faults and escalate to human engineers

At SEOS Design, we work with AI agents in our own processes and for clients, in close collaboration with our sister brand AI Partner, which specialises in AI implementations for businesses. Want to discuss how agents can automate workflows in your organisation? Get in touch.

Risks and limitations

AI agents are powerful – which also exposes them to specific risks:

• Hallucination in action – When an agent acts on incorrect information, the consequences can be more serious than when an LLM gives a wrong answer
• Prompt injection – Malicious instructions hidden in data the agent reads can manipulate its behaviour
• Misaligned learning – Agents that optimise for a metric can find unexpected shortcuts that defeat the original intention
• Cascading errors in multi-agent systems – An error in one agent can spread and grow as it passes through the system

Agents are powerful but not infallible – human oversight is essential.