AI assistant or AI agent – what is the difference?

An assistant answers and suggests. An agent can plan multiple steps and use tools to execute them. The difference might sound simple, but it has major consequences for permissions, testing, and accountability.

Many organizations do not need the most autonomous solution. They need the right degree of agency for a clearly defined task. The more the system is allowed to do, the greater the requirements for checkpoints, logging, and human approval.

Four concepts that are often confused

Chat primarily describes the interface. The user types a question and gets an answer. Behind the chat, there can be anything from a general language model to a permission-controlled solution with the business's own sources.

AI Assistant supports a user within a defined area. It can search, summarize, create drafts, and suggest next steps, but the user is normally expected to review and decide.

AI Agent is given a goal and can plan or execute multiple sub-steps. For example, it can retrieve information, call a system, create a supporting document, and deliver a result. The agent's scope of action depends on the tools and permissions it is granted.

Traditional automation follows predetermined rules and flows. It is often best when the process is stable, the input data is structured, and the outcome must be deterministic.

The concepts do not describe a strict technical standard. Two vendors may call similar functions by different names. Therefore, define requirements for what the solution is allowed to read, assess, and execute – not what name it has.

What does an AI assistant do?

An assistant is suitable when a human leads the work and needs faster access to information or a first draft. It can, for example:

• answer questions based on internal documents

• summarize a report with citations

• compare two contracts and highlight differences

• create a draft for a customer response

• suggest classification or priority for a ticket

The value often lies in reducing search and preparation time. The user retains control over what is sent, decided, or published. This makes the solution easier to implement in tasks where quality needs to be assessed in context.

Yet, an assistant is not risk-free. It can access the wrong source, formulate an incorrect response, or reproduce sensitive information. Permissions, testing, and logging are needed even when the system does not perform actions.

How to connect a secure chat to your business's own data

What does an AI agent do?

An agent connects the language model's reasoning to tools. It can choose the next action based on a goal and the result of the previous step. For example, an agent for a support flow can identify the ticket, retrieve customer details, suggest a solution, create a response, and prepare an update in the ticketing system.

The key is not that the agent "thinks for itself," but that it is allowed to choose between permitted actions. Every tool should have a clear contract: what data it can read, what it can change, which parameters are allowed, and when a human approval is required.

The risk profile increases when errors can have consequences outside the chat. An incorrect text response can be corrected before it is used. An incorrect system change, order, or external communication can be harder to undo.

The more a solution is allowed to do, the clearer accountability, permissions, and checkpoints must be.

When is regular automation enough?

If every step can be described with stable rules, regular automation is often more robust and cheaper to maintain. An invoice with the correct format can be sent through a fixed validation flow. A form can create a task according to known conditions. An integration can transfer data without a language model interpreting it.

Generative components are relevant when the input data is unstructured or varied, such as emails, free-form text, and documents. A good system can combine the methods: the model interprets or suggests, while rules check format, amount, permissions, and final action.

This combination reduces dependency on the model's free output. OWASP points out that unsafe handling of model output can create vulnerabilities when text is used as an instruction to other systems.[1] Output should therefore be treated as untrusted input until it has been validated.

Differences in access and responsibility

A chat usually needs read access to a limited amount of information. An assistant may also need to create drafts or save work materials. An agent may need write access to ERP systems, start processes, or communicate externally.

Do not give the agent a shared technical identity with broader access than the users. Use the principle of least privilege, separate tools by risk, and let access follow the user or service role performing the task.

Responsibility needs to be defined on three levels:

• the business owner is responsible for purpose, metrics, and permitted use cases

• the system owner is responsible for configuration, integrations, and maintenance

• the user is responsible for the checks and approvals included in their role

The supplier's responsibility must be stated in agreements, but the business cannot contract away its own responsibility for how the solution is used.

Permission-controlled AI with Microsoft Entra ID – how access works

When is human approval needed?

Human approval should be placed before actions with high consequence, low reversibility, or unclear supporting evidence. Examples include payments, contracts, decisions about an individual, publishing, deletion, and changes in business-critical systems.

The approval should show what the agent intends to do and what supporting evidence it used. An "approve" button provides little control if the user cannot understand the consequence.

Automation without manual approval can be reasonable for low-risk actions with clear boundaries, good test coverage, and the ability to quickly stop or roll back. The decision should be documented and reviewed when the flow changes.

AI Act, NIS2, DORA & GDPR – what requirements affect your company's AI solution?

Examples of the right level for different tasks

Answer

An internal chat finds a routine and shows the source. Fits an assistant.

What is RAG? How companies use their own data in a language model

Suggest

The system suggests a category and response to an incoming ticket. A human selects and sends.

Prepare

An agent retrieves data from multiple systems and creates complete supporting documentation, but is not allowed to register the decision.

Execute

An agent updates a system after validation and approval. Requires clear tool boundaries, logging, and the ability to roll back.

Automate

A rule-based flow performs the same checks every time and only forwards exceptions to a case officer.

How to choose the right level

Ask the questions in order:

1. Does the solution only need to find and formulate information?

2. Does it need to suggest a decision or next step?

3. Does it need to prepare an action in another system?

4. Does it need to execute the action without approval?

For every yes, document data, permissions, consequence of failure, human control, and recovery. Start with the lowest level that can generate value. Add agency only when quality is tested and responsibility is clear.

NIST recommends that risks with generative systems be assessed across the entire lifecycle and in the actual context of use.[2] This supports a gradual implementation: first support, then controlled action where there is a proven need.

How to choose the right use case for your first AI project

Checklist before giving an AI agent tools

• Are the goal and permitted actions precisely defined?

• Does every tool use the least privilege possible?

• Is the model's output validated before a system call?

• Do irreversible or sensitive actions require approval?

• Are supporting evidence and consequences clearly shown to the approver?

• Are suggestions, tool calls, and results all logged?

• Are there monetary limits, rate limits, and kill switches?

• Can an incorrect action be rolled back?

• Is the flow tested against manipulated input and unexpected responses?

• Is there a named owner after launch?

AI in the executive team: 10 questions that take you from idea to business value

Sources

1. OWASP, Top 10 for LLM and GenAI Applications:
   https://genai.owasp.org/

2. NIST, Generative AI Profile:
   https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence

3. European Commission, AI Act:
   https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai