AI agents are software systems that use an AI model to pursue a goal, choosing steps and using tools with some independence. They are worth considering when a task involves ambiguous context, unstructured information, or changing conditions that make fixed rules hard to maintain. For a predictable task—or one that a single model response with retrieval can handle—a simpler system is often easier to control.
What is an AI agent?
There is no single industry-wide definition of “agent.” In this article, an AI agent means a system in which an AI model can decide what to do next, use tools, and take actions toward a goal, within instructions and permissions set by its designers. OpenAI’s guide to building agents describes a basic agent as combining a model, tools, and instructions.
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The model helps select or sequence actions; tools connect it to information and external systems; and instructions define its role, boundaries, and guardrails. Tools may retrieve data, perform actions, or coordinate work. The tools available—and the permissions attached to them—determine what the system can actually do.
A chatbot is not automatically an agent. A chatbot that only generates replies is not directing a process or taking tool-based actions. A conversational interface can, however, sit on top of an agent if the system behind it decides which steps or tools to use. Google Cloud’s overview uses a broader framing in which agents can reason, plan, observe, and act, and distinguishes agents from assistants and bots by their relative autonomy and supervision; that is one vendor’s framing, not a universal taxonomy. Google Cloud’s overview of AI agents was last updated April 2, 2026.
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How is an agent different from a workflow?
A useful distinction is who directs the process. A conventional workflow follows a sequence defined in code. An agent can choose its next step dynamically in response to the task or what it learns along the way. Anthropic describes these as different approaches: “Workflows are systems where LLMs and tools are orchestrated through predefined code paths,” while agents let models dynamically direct their processes and tool use. Anthropic’s guide, published December 19, 2024, also cautions that “agent” is used in different ways.
| Approach | Who directs the steps? | Best fit |
|---|---|---|
| Single model call, possibly with retrieval | The application makes one request; the model returns a response based on the input and retrieved information. | A question or task that can be handled in one pass. |
| Predefined workflow | Application code determines the sequence and conditions. | A well-defined process with stable rules and steps. |
| Agent | The model selects steps or tools dynamically within its instructions and permissions. | A task where the right next step depends on ambiguous context or intermediate results. |
These approaches are not simply a scale from “less AI” to “more AI.” A workflow can include a model and tools without being an agent in this sense: the code still decides what happens next. The important question is whether dynamic model-directed choices are needed.
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When might an AI agent be worth using?
Consider an agent when a task combines information gathering, judgment, and actions, and the sequence cannot be reliably specified in advance. Agents may be useful when rules have become complex or costly to maintain, or when the system must interpret unstructured information and adapt to what it finds. OpenAI’s fraud-analysis example contrasts preset criteria with contextual evaluation; it illustrates a possible use, not proof of measured business results. OpenAI’s guide discusses these kinds of use cases.
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- Changing conditions: The right procedure depends on information discovered during the task.
- Multiple possible tools: The system needs to choose among searches, data sources, or actions based on intermediate results.
- Brittle rules: A conventional ruleset is difficult to update because it has accumulated many exceptions.
These are reasons to test an agent, not guarantees that one will outperform a simpler approach. Start with the simplest solution likely to work: Anthropic notes that a single model call with retrieval and examples can be enough for many applications, and that agentic systems can trade extra latency and cost for task performance. Anthropic’s guide recommends using workflows when predictability and consistency matter for a well-defined task.
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When is a workflow or single model call the better choice?
Keep the design simpler when the task has a clear goal, stable inputs, and a dependable sequence of steps. If a standard workflow can cover the expected cases, an agent’s added decisions may create more opportunity for error without solving a real problem. If one model response, perhaps grounded with retrieval, is enough, adding repeated model calls and tool selection may only make the system slower, more expensive, or harder to inspect.
- Prefer a single model call with retrieval when one response grounded in relevant information completes the task.
- Prefer a workflow when the sequence is known and should be executed consistently.
- Evaluate an agent when the system needs to choose or revise steps based on context it encounters.
How to decide whether an agent is worth prototyping
- Describe the task and its exceptions. Identify what the system receives, what a successful result looks like, and where the process currently needs judgment.
- Check whether the sequence is genuinely variable. Ask whether the system must choose among tools or next steps based on intermediate results, or whether fixed conditions cover the work.
- Set a simpler baseline. Measure the task using the best practical alternative, such as a fixed workflow or a single model call with retrieval.
- Test the agent against the same cases. Compare task success and error handling, as well as latency and cost. Record where it fails and whether it can recover or hand off to a person.
- Expand permissions only if results justify them. Begin with limited access; keep approval requirements appropriate to the consequences of each action.
When comparing implementations, assess flexibility, predictability and control, recovery, cost and latency, and integration and maintenance. In particular, check whether a team can inspect the agent’s actions, stop execution, restrict tool access, and keep its prompts and execution understandable. These are evaluation criteria, not evidence that a particular agent or product performs better.
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OpenAI recommends setting evaluations and a performance baseline, then optimizing cost and latency while keeping accuracy acceptable. The comparison should be made on the task that matters to you; an agent is only justified if its improvement over the simpler baseline warrants the additional complexity. OpenAI’s guide covers agent evaluation and optimization.
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When a system can take actions, a misunderstanding can have consequences beyond a poor answer. Anthropic identifies risks including misread intent, unintended actions, and prompt injection—attempts to manipulate a system through instructions embedded in content it processes. Its guidance on trustworthy agents emphasizes human control, transparency, secure interactions, alignment with user values, and privacy. Anthropic’s discussion of trustworthy agents describes these concerns.
- Limit permissions: Give the agent access only to the tools and data it needs for the task.
- Keep consequential actions under human control: Require approval before actions with significant or hard-to-reverse effects.
- Make behavior visible: Provide a way to inspect what the agent plans or has done and to stop it.
- Protect sensitive information: Consider what data tools can expose and how information is handled.
Anthropic’s safety framework stresses stronger oversight before high-stakes decisions, alongside transparency and privacy protections. Anthropic’s framework for developing safe and trustworthy agents provides further guidance. The appropriate safeguards depend on what the agent can access and what its actions could affect.
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