The Tool Desk
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Those labels are not a universal technical standard. To choose well, look past the product name and check what it can do, how much control you retain, and what happens if it gets something wrong.
How AI assistants, agents, and workflows differ
The practical distinction is who directs the work and how the next step is chosen. Google Cloud frames assistants as systems that collaborate directly with users, while agents pursue goals through reasoning, planning, memory, and some autonomy. Google Cloud also describes an assistant as a possible designed form of agent, so the categories can overlap. Microsoft’s guidance likewise distinguishes agents by their ability to plan and use tools, but a product called an agent may still be limited to a bounded task.
Anthropic offers a useful architectural contrast: a workflow uses an AI model and tools along predefined code paths, while an agent dynamically directs its process and tool use. A workflow can still contain decision branches; the key is that its path is explicitly designed rather than chosen on the fly by the model.
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| Decision point | Assistant-oriented use | Agent-oriented use | Fixed workflow or function |
|---|---|---|---|
| Who directs the work? | The person prompts, reviews, and decides what to do next. | The person gives a goal; the system may choose intermediate steps. | The developer or operator defines the steps in advance. |
| Typical task | Conversation, information, drafting, recommendations, or bounded help. | Open-ended or multi-step work involving decisions and tool use. | Repeated tasks with stable inputs and predictable steps. |
| Tool use | May use tools, but interaction with the person remains central. | Selects and uses available tools to retrieve information or act toward the goal. | Calls specified functions or services in a defined order. |
| Adapting to the situation | Responds to each new prompt from the user. | Can change its process as task state or findings change. | Follows its explicit path unless programmed branches apply. |
| Oversight | The user reviews suggestions or outputs. | Requires suitable permissions, guardrails, evaluation, and escalation for consequential actions. | Requires testing of the explicit logic and known exceptions. |
For practical product comparisons, ask what the system actually does rather than relying on the label. Google Cloud, Microsoft Learn, and Anthropic all describe distinctions that are useful for design, but none makes “assistant” and “agent” a single universally enforced taxonomy. Google Cloud’s overview of AI agents, Microsoft Learn’s introduction to agents, and Anthropic’s discussion of agent design provide their respective framings.
When to use an AI assistant
Choose an assistant when the task benefits from an exchange with a person who stays in charge. It is a natural fit for explaining a topic, drafting or revising text, finding information, summarizing material, or generating recommendations for you to evaluate.
For example, Microsoft describes Copilot as an assistant built into Microsoft 365 applications, where it can help summarize an email or highlight key points in a meeting. These tasks benefit from convenient help, but a person still determines what to do with the result. Microsoft Learn’s introduction to agents describes this assistant-oriented pattern.
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When to consider an AI agent
Consider an agent when a task has a goal but its intermediate steps depend on what the system finds, and the task benefits from using tools. Examples might include gathering information from several sources, interpreting documents, or handling a customer-service case that depends on context rather than a simple rule. OpenAI identifies complex decisions, unwieldy rule sets, and heavy reliance on unstructured data as promising patterns; that makes them candidates to test, not guarantees of success.
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OpenAI’s implementation documentation describes different ways to build agentic systems, including a managed Agents API, an Agents SDK that runs in a developer’s application, and the Responses API for direct model use or custom orchestration. These choices differ in runtime and state management, integration effort, and tool execution; they do not imply that all products marketed as agents share one architecture. OpenAI’s practical guide to building agents covers use cases, components, evaluation, and guardrails, while its Agents API documentation outlines implementation options.
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When a workflow or function is the better choice
Use a deterministic workflow or function when the steps are well defined and can be handled reliably with explicit logic. If a task is essentially “take this input, apply these rules, then call this service,” an agent may add unnecessary variability. Microsoft Learn states: “If you can write a function to handle the task, do that instead of using an AI agent.” Anthropic similarly recommends starting with the simplest solution that meets the task’s needs. Microsoft Agent Framework overview gives its workflow-versus-agent guidance.
This does not mean every task must be fully deterministic. A workflow may use an AI model for a bounded step, such as classifying a message, while explicit code controls what happens next. The relevant question is whether the model needs to choose the process and tools dynamically, or whether the process can be designed and tested in advance.
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- Decide whether the steps can be specified in advance. Stable inputs and predictable steps point toward a function or workflow; a goal whose next steps depend on findings may favor an agent.
- Check whether tool choice needs to adapt. If the system must select among tools or decide what to do next as task state changes, agent-like behavior may help. If not, use an assistant or fixed path.
- Set the action boundary. List the information the system may read and actions it may take. Give it only the access needed for the task, and decide which actions require human approval.
- Assess the cost of an error. A wrong draft suggestion is different from a mistaken record change or message sent to a customer. The more consequential the action, the stronger the review, testing, and escalation controls should be.
- Compare against a simpler option. Test whether an assistant, workflow, or function can meet the required quality before adding an agent’s flexibility.
Trade-offs and oversight for agentic systems
Agents can be useful when exceptions are hard to enumerate, but their flexibility is not free: agentic systems may trade greater latency and cost for improved task performance. Anthropic recommends adding complexity only when it is needed. OpenAI recommends establishing an evaluation baseline, checking whether the system meets its accuracy target, and then optimizing cost and latency. The right choice depends on measured performance for the specific task, not the “agent” label.
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For actions that affect records, send messages, or trigger processes, define guardrails and a route back to a person when execution fails. OpenAI describes handing control back to the user when an agent cannot complete execution. Microsoft places responsibility on developers to test for their use cases and review data flows, permissions, quality, reliability, security, and trustworthiness. These are design responsibilities, not properties automatically supplied by calling a system an agent. OpenAI’s agent guide and Microsoft’s framework overview discuss these controls.
Agent design options depend on the platform
Implementation choices can shift how much orchestration and infrastructure a developer manages. On Microsoft’s platform, a declarative agent customizes Copilot through instructions, data, and actions using Microsoft’s built-in infrastructure. A custom engine agent allows more control over orchestration and models, while requiring additional hosting and security work. This is a Microsoft-specific distinction, but it illustrates the broader trade-off between a managed, bounded experience and a more customizable system. Microsoft Learn’s agent introduction describes these types.
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