Insights / Intelligent agents
When does a task
need an AI agent?
A practical guide to choosing between automation, an AI assistant and an agent, with a worked example, human oversight and evaluation questions.
An impressive demonstration can make an agent look like the obvious next step. The more useful starting point is the work itself: what needs to happen, which decisions vary, and what would count as a good result?
This guide offers a way to frame that decision. It is an introductory design perspective from IGIQANT, with an illustrative example rather than a report of a deployed product.
The short answer
Consider an AI agent when a task needs several steps, the next step depends on what is discovered, and the result can be checked. Choose a fixed workflow when the sequence is predictable. Keep the scope small enough that someone can understand and evaluate what happened.
1. Choose the system around the task
Terminology varies. Anthropic’s architecture guide distinguishes a workflow with predetermined execution paths from an agent whose model chooses how to proceed. This distinction concerns how work is coordinated; it does not establish that an agent is more suitable for every task.
| Approach | Who determines the next step? | Illustrative use |
|---|---|---|
| Fixed automation | Rules or a defined sequence. | Move an approved file into a designated folder. |
| AI assistant | The person directs the interaction. | Rewrite a supplied paragraph for clarity. |
| AI agent | The model selects steps within the system’s permissions. | Investigate an open question using approved sources, then prepare a brief. |
These categories can overlap. A workflow can contain an agent, and a conversational assistant can offer agent capabilities. For a first design discussion, focus on the decisions delegated to software instead of the product label.
2. Start with a concrete example
Imagine preparing an internal briefing from a folder of approved project notes. The goal is a one-page account of the current position, open questions and conflicting information. Nothing is sent outside the team.
If the same three documents always supply the same fields, a fixed workflow may be enough. If the question changes each time, an assistant could help a person select and interpret the material. An agent becomes worth testing when it needs to choose which notes to inspect next and follow up on gaps within that folder.
- 01DefineA question and approved source folder
- 02InvestigateRead, compare and identify gaps
- 03PrepareA draft with source references
- 04ReviewA person checks and decides what to share
For this example, success means that a reviewer can trace factual statements to the supplied notes, see unresolved contradictions, and revise the brief without reconstructing the entire investigation. A polished paragraph with untraceable claims would fail that definition.
3. Make human control specific
A request to “keep a human in the loop” leaves important questions unanswered. Who reviews the result? What information do they see? Which decisions are theirs to make? NIST’s AI RMF Core calls for defined responsibilities for human–AI oversight and continuing risk management throughout the system lifecycle.
For the briefing example, put a short review record beside the draft: the source for each important claim, gaps in the material, and the proposed audience. Give the reviewer a clear way to return the work for correction. Do not make acceptance the only convenient action.
Access controls also matter. OWASP identifies excessive functionality, permissions and autonomy as sources of excessive agency. Limit available tools and enforce authorization in the systems those tools access. For this example, read access to the approved folder is sufficient; sending messages or modifying source documents is outside the task.
A human approval step can help with consequential actions, but it does not replace permission checks. The person needs a reviewable result, while the underlying system needs enforceable limits.
4. Evaluate the work, including failures
An agent can produce a convincing final response while taking an incorrect path. Anthropic’s guide to agent evaluations treats the task, the execution trace and the resulting state as distinct parts of an evaluation. Examine what the system did as well as what it said.
For the proposed briefing pilot, use a small set of approved examples that includes a missing note, contradictory notes and an ambiguous question. Keep a reference review for each case. Compare the agent with the current manual process or a simpler workflow on the same material.
- Grounding: can the important claims be traced to the supplied material?
- Boundaries: did the system stay within the allowed sources and actions?
- Uncertainty: did it expose gaps instead of filling them with unsupported claims?
- Review effort: how much checking and correction did a person need?
- Resources: were the time and operating cost worthwhile for this task?
Set acceptance criteria before running the comparison. A good result on one example is a reason to investigate further, not a measured claim of general reliability.
5. Write the pilot decision in plain language
Before building, finish this sentence: “We want to test whether this system can help [person] complete [task], using [approved information], while [person or process] remains responsible for [decision].”
Then name the result you will inspect, the actions the system cannot take, and the conditions that end the trial. If those details remain unclear, narrow the task before expanding autonomy.
IGIQANT’s perspective is to connect technical capability with useful human work. For this kind of pilot, the relevant question is whether the resulting work becomes easier to evaluate and use. That is something a focused experiment can test.
Explore our technology direction and the principles behind IGIQANT.
Sources and editorial note
- Anthropic — Building effective agents
Architecture distinctions; published December 2024. Used for the conceptual distinction, not current tooling recommendations.
- NIST — AI RMF 1.0: Core
Roles, oversight and ongoing evaluation. A voluntary framework; this article does not claim certification or compliance.
- OWASP — LLM06:2025 Excessive Agency
Limiting tool functionality, permissions and autonomy.
- Anthropic — Demystifying evals for AI agents
Evaluating agent behavior and results; published January 2026.
Prepared with AI assistance for IGIQANT. The briefing scenario and pilot questions are illustrative editorial examples. They are not results from an IGIQANT deployment. Sources checked on 10 September 2026.