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For business teams, the practical difference is how much authority the AI has. An assistant responds to a person by answering questions, drafting, summarizing, searching, or suggesting. An agent can go further: take a goal through a workflow, select and use tools, and act in connected systems within permissions and human-defined guardrails. Because vendors sometimes use the labels interchangeably, compare what a system can actually do—not just what it is called.
What is an AI assistant?
An AI assistant is a user-facing helper. An employee asks it to summarize a meeting, find information in company material, draft a reply, or suggest next steps. The employee generally reviews the output and carries out any consequential action.
The key feature is not necessarily a chat window. It is the assistant’s role in the work: it helps a person understand or prepare something, while the person remains responsible for deciding what happens next.
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What is an AI agent?
An agent connects AI reasoning to tools and workflow execution. OpenAI defines an AI agent as “a system that can plan, decide, and act independently to achieve a goal while operating within guardrails set by humans.” Its guide describes three elements: a model, tools such as connectors or APIs, and guardrails. OpenAI’s business leader’s guide explains the definition.
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In practice, an agent may retrieve relevant context, choose a next step, and use an authorized tool to create a task, send a permitted message, or update a record. It can select and sequence tools as a workflow proceeds rather than simply returning one answer. OpenAI’s practical guide to building agents distinguishes agents from applications that use a language model but do not let it control workflow execution, such as a single-turn question-and-answer app.
Assistant vs. agent: the business difference
| What to compare | Assistant-style use | Agent-style use |
|---|---|---|
| Workflow | Responds to a request; a person decides and performs the next step. | Can choose and sequence steps toward a goal within defined limits. |
| Tools and actions | May search or prepare content for a person to review. | May use connected tools to create, send, update, or trigger actions, subject to access and controls. |
| Human involvement | The person typically reviews the output and performs consequential actions. | A person may set the goal and guardrails, approve particular actions, handle exceptions, or take control. |
| Risk focus | Review whether the response is useful and appropriate before using it. | Also assess what systems it can access, what it can change, and who is accountable for its operation. |
These are capability patterns, not strict product categories. Microsoft describes agents as specialized AI assistants, and its documentation presents agents as extending assistants with specialized knowledge, data sources, and actions. A chat interface alone does not make a system an agent; the important question is whether it controls workflow steps and can act through tools. See Microsoft’s introduction to agents.
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How to tell which one a team needs
Use an assistant when a person should own the action
Assistant-style help suits work where AI can reduce reading or drafting effort but an employee should decide what to do and execute it. Examples include summarizing a meeting, drafting a customer response for review, searching internal material, or proposing next steps.
Consider an agent when a bounded workflow can be delegated
An agent may fit a repeatable process that needs information from connected systems and a sequence of permitted actions. For example, it could gather lead information, determine a next step, and create a task or send an approved message. Microsoft documents capabilities such as extracting data, creating tasks or files, sending messages, and automating lead-management steps. These are examples of possible functions, not evidence that a particular implementation will be accurate or produce specific business results.
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Choose based on the work, not the label
Before selecting a product or approach, map the workflow: what starts it, what information it needs, what decisions it must make, which systems it may touch, and where a person must review or intervene. If the AI only returns an answer for an employee to use, assistant is the clearer description. If it can control steps and execute actions across tools, agent is more informative—even if the vendor calls it an assistant.
What to evaluate before giving an agent access
Workflow autonomy
Establish whether the system can only respond or can plan and sequence steps toward the goal. Define which decisions it may make and which require a person. The more open-ended the task, the harder it is to predict its actions.
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Tools, data, and permissions
List the knowledge sources and connected systems it can access, then specify the actions allowed in each. Searching a knowledge base is different from sending a customer message or changing a customer record. Use permissions scoped to the workflow rather than granting broad access by default.
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Decide where a person must approve an action, how exceptions are routed, and how an employee can pause or take over. A workflow should make clear what the agent has done and what remains for a person. Approval points matter most where an action has meaningful consequences for customers, colleagues, money, or business records.
Ownership and ongoing governance
Assign an accountable owner for access, oversight, and changes after release. Governance should reflect the agent’s actual behavior and risk, not just the fact that it uses AI. Microsoft’s guidance on governing agents by risk addresses matching controls to what an agent does.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Agent-building approaches: declarative or custom engine
For teams building agents in Microsoft’s ecosystem, the implementation choice affects how much of the orchestration the host platform manages. Microsoft describes two broad approaches:
| Approach | How it works | When it may fit |
|---|---|---|
| Declarative agent | Uses instructions, knowledge, and actions with the host’s orchestrator and models. | When the built-in orchestration and capabilities cover the workflow’s needs. |
| Custom-engine agent | Allows more customization of orchestration, models, and autonomy behavior. | When the workflow needs capabilities beyond the host’s built-in approach, with additional security and responsible-use work planned. |
These are implementation patterns, not a universal ranking. More customization can provide flexibility, but the organization must also take on the corresponding security and responsible-use planning. See Microsoft’s comparison of declarative and custom engine agents.
Check deployment prerequisites before rollout
Access, licensing, and deployment requirements depend on the vendor, plan, and configuration. Confirm the current prerequisites for the organization’s geography and tenant before committing to a design. Microsoft maintains prerequisites for managing agents in Microsoft 365; those requirements should not be assumed to apply to other platforms.
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