Pick by where the bottleneck sits. An assistant makes a person faster, and an agent takes work off the person's desk. If the constraint is expertise on varied, hard cases, give the experts an assistant. If the constraint is the volume of similar cases flowing through a queue, the queue needs an agent, with a person on what the agent should not decide.
What is an AI assistant, and what is an AI agent?
IBM's explainer on the question defines an AI assistant as "an intelligent application that understands natural language commands and uses a conversational AI interface to complete tasks for a user," and an AI agent as "a system or program that can autonomously complete tasks on behalf of users or other systems by planning its own workflow and using available tools." OpenAI's practical guide to building agents draws the same line from the other side: applications that use a language model but do not let it control how a workflow runs, such as simple chatbots, are not agents.
"Copilot" is a third word in the mix. As a category, a copilot is an assistant built into one application, drafting and suggesting while the person in that application decides. Some products sold under assistant or copilot names now include agent features, so judge the behavior, not the label.
For an operation, the cleanest test is ownership. Ask who is accountable for the case once the AI has touched it. With an assistant, the person who asked still is. With an agent, the process is: the agent completes the case, and a named person owns the exceptions.
Side by side
| Dimension | AI assistant | AI agent |
|---|---|---|
| Who starts the work | A person, with a request | An event: a case arrives in a queue or a system |
| Who owns the result | The person who asked | The process; the agent completes it and a person owns its exceptions |
| Where the output lands | In front of the person: a draft, an answer, a summary | In the system of record: a status change, an update, a sent reply |
| Autonomy | One request at a time | Several steps in a row, inside defined limits |
| System access | Usually read access, through the person | Read and write access, through its own scoped permissions |
| Who catches an error | The person, before using the output | Guardrails, tests and exception routing |
| What it scales with | The number of people using it | The volume of cases |
| What to measure | Time saved per person, adoption | Cases completed, exception rate, error rate, time to resolution |
| Oversight | Built into the user's own review | Designed in advance: triggers, owners, response times |
| Main risk | Bad advice a person might act on | Wrong actions taken at volume |
Plenty of tools sit between the two columns, and one product can behave like either, depending on how it is configured and what it is allowed to touch.
When an AI assistant is the right choice
- The work varies more than it repeats. Investigating a complex dispute, preparing a negotiation or drafting a policy exception all differ case to case. An assistant that pulls history and drafts options helps; an agent would have little to repeat.
- A person must stay accountable for each decision. Where professional, legal or customer-relationship reasons require a human to own every outcome, keep the AI in an assisting role.
- The process is not yet defined. Agents need known steps and access to systems. If the process lives in people's heads, start by helping those people and learn where the repeatable parts are.
- You need a cheap, reversible start. Assistants are quick to try and easy to retire.
When an AI agent is the right choice
- The same kind of case arrives in volume. Change requests, routine inquiries, document checks and status updates are agent work when they follow a recognizable pattern.
- The right outcome can be checked. If a reviewer can tell whether the case was handled correctly, the agent can be tested against past cases before launch and measured after it.
- The bottleneck is throughput. Giving every clerk an assistant still leaves each clerk handling every case by hand. An agent removes the routine cases from the queue entirely.
- The steps and systems are known. The agent needs a defined agent workflow and permissions scoped to what the process requires.
Agents carry risks assistants do not. The OWASP GenAI Security Project's entry on "Excessive Agency" describes the damage an LLM system can do when it has more functionality, permissions or autonomy than it needs, and it recommends human approval for high-impact actions. That is why every agent needs a human in the loop designed before launch, with clear triggers, owners and response times.
How they combine
The two fit together better than they compete. Consider a hypothetical logistics company's delivery-exception queue. An agent handles the routine cases: rescheduling missed deliveries, answering where-is-my-order requests, updating addresses that pass verification. Damaged-goods claims and disputed charges go to a person. That person works with an assistant that summarizes the shipment's history, pulls the photos and the contract terms, and drafts a response for them to edit and send. The agent cleared the volume. The assistant sped up the hard cases, and the decisions on them stayed with the person.
The same split shows up when buyers compare agents with bots. RPA vs AI agents covers the boundary on the other side: where a deterministic script is a better fit than either.
Where buyers get the choice wrong
Three mistakes recur. The first is buying an assistant and expecting agent results: licenses get rolled out, people use them, and the queue stays the same size because every case still passes through a person's hands. The second is calling a chat assistant an agent because it can call one tool; ask whether it completes a case in a system of record without someone pressing send. The third is launching an agent without anyone owning its exceptions, which turns the first unusual case into an incident.
For a COO deciding where to start, the guide to AI agents for business sets out how to tell which steps an agent can own, and the AI for business operations guide covers choosing the process in the first place.