AI Assistant vs. AI Agent: Who Chooses the Next Task?

An evidence-based guide to comparing how much follow-through an AI product can own.

People use “AI assistant” and “AI agent” in different ways. The label alone does not tell you what a product can safely do. A more useful question is: after you make one request, who chooses the next action?

Anthropic’s guide distinguishes predetermined workflows from systems where a model directs its own process and tool use. The guide notes that parts of its tooling discussion have changed since publication, so use it here for the conceptual distinction, not as a current framework recommendation. OpenAI’s Agents SDK describes an agent as a model configured with instructions, tools, and optional runtime behavior.

For someone choosing a product, the practical difference is not a name. It is what the product decides, what it can change, and how you can check the result.

Four questions to ask before relying on an AI agent

1. Who chooses the next action?

A chat assistant may answer one request and wait. A tool-using agent may carry a task through several steps. A manager-style product is designed to keep identifying the next useful piece of work within the permission you have delegated.

These patterns overlap. Ask to see the product make its decision, and check whether the user must provide a new task-specific goal or budget before it continues.

2. What can it change outside the conversation?

A tool call can draft an email, create a file, or submit a form. Those are different actions with different consequences. Ask which connected accounts it can use, what each action changes, and how to stop future work.

Tool access alone does not show that the product has authority to use every tool for every purpose.

3. How does it verify that the action worked?

A message saying “done” is not proof that an external service accepted the action. Look for a provider confirmation, a changed record, a published URL, or another result that you can inspect.

Keep these states separate:

• the model suggested an action;
• a tool request was sent;
• an external service accepted the request;
• the intended result was verified;
• a sale was recorded;
• money was settled after fees, refunds, and costs.

4. What happens when the result is uncertain?

A dependable system should say what it could not confirm. It should not repeat a submission that may already have succeeded, or turn a missing receipt into a claim that nothing happened.

A useful product makes uncertainty visible and gives the person a way to pause or stop further work.

What “manager” means in Life Manager

Life Manager uses manager as a product-design term: choose work from the context and permissions already provided, act within those boundaries, verify the external result, and report what happened. Users do not have to enter a task-specific goal or budget before the manager can begin, and Telegram is optional.

That is our design intent, not a universal industry definition or a claim that the hosted product has completed every step. The early pilot signup page is live. The complete hosted work-and-report cycle and any user earnings are still unverified.

The pilot page explains its data conditions before activation. Necessary work inputs and results are sent to Maritime; its Privacy Policy says AI-credit model calls may be retained indefinitely, including after account deletion. Read those terms and do not send sensitive information. Revenue or profit is not guaranteed.

A practical test

When you compare two products, give each the same small, reversible task. Record what the product chose, what it actually changed, what the provider confirmed, how long it took, and what it cost. If a result is unknown, keep it unknown. If the product stops at a suggestion, that may still be useful—just measure it as assistance rather than completed work.

Want to inspect the early pilot? See Life Manager’s current pilot page. It shows the current signup and data-retention terms; the full hosted work cycle is still being tested.

Sources

• Anthropic, Building effective agents (the page notes that its tooling discussion has changed; the workflow/agent distinction is used here only as a conceptual reference).
• OpenAI Agents SDK, Agents.
• Life Manager’s early pilot page.
• Maritime Privacy Policy.