How do AI agents work? From goal to controlled outcome

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AI agents combine an AI model with instructions, context and sometimes external tools. When an agent receives a goal, it interprets the task, chooses a next step, performs that step and uses the result to decide what should happen next. The process may be a simple fixed sequence or a series of controlled loops.

A language model is often the engine, but it is not the whole agent. The surrounding system decides which instructions take priority, what information may be retrieved, which tools are available and when work must stop or be handed to a person.

A typical agent workflow

1. Receive a goal

The goal must be concrete. “Help with sales” is difficult to evaluate. “Summarise the customer’s needs from these notes and propose three follow-up questions” is clearer. The system can check that required inputs are present before starting.

2. Create or follow a plan

Some agents follow a fixed sequence: read material, extract facts, draft an output and validate its format. Others choose their next step dynamically. Dynamic planning is flexible but less predictable. For recurring business tasks, a clear workflow is often cheaper and easier to test than open-ended planning.

3. Retrieve relevant knowledge

The agent may use the current conversation or retrieve material from a knowledge base. With RAG, the system searches for relevant passages and supplies them to the model. A good agent identifies its sources and says when the available evidence is insufficient.

4. Use a tool

A tool is a limited function such as searching a database, performing a calculation or creating a ticket. Tools need precise descriptions and limited permissions. Reading data is normally less risky than sending messages, editing records or transferring money.

5. Check the result

Checks may be technical and editorial. The system can validate required fields, while another step can compare claims with source material. Important applications also require human review.

6. Stop or ask for help

A well-designed agent knows when to stop. If a source is missing, a tool fails or a request exceeds its authority, it should explain the problem and request a decision instead of improvising.

What does “memory” mean?

Short-term memory may be the conversation and intermediate results in one run. Long-term memory may include stored preferences, previous cases or data in an external system. Long-term memory raises privacy and quality questions: what is saved, for how long, who can see it and how outdated information is corrected. Do not retain data merely because you can.

Example: a meeting follow-up agent

A user uploads meeting notes. The agent extracts decisions, actions, owners and dates. Missing owners or dates are labelled “needs completion”. The output follows a fixed table. The meeting owner reviews it before distribution. If the agent later gains permission to create project tasks, it should first show a preview and request approval.

Why agents fail

Errors can come from the model, unclear instructions, outdated sources, unexpected tool responses or several small failures interacting. An agent can also encounter prompt injection: malicious text in a document or webpage attempts to override its rules. Instructions, external data and tool outputs must therefore be treated differently, and permissions should be minimal.

A practical rule

The more freedom an agent has, the stronger its testing, logging, permission controls and human checkpoints must be. Begin with reading and recommendations. Add actions only when the process is stable and the consequences of failure are manageable.

In summary

An AI agent works through a cycle of goal, next step, information, tools and checks. Models provide flexibility; system design provides control. A useful agent has a clear workflow and knows when to stop.

Sources

Last reviewed: 16 September 2026.