How does an agent differ from a workflow?
Anthropic’s architectural explanation distinguishes predefined workflows from agents that dynamically direct their process and tool use. Terminology varies across products, so observing behaviour is more informative than reading a label. A chat box alone does not establish that a system is an agent. [1]
A scheduled process that downloads one file and adds it to a fixed table may follow a completely predetermined route. A system choosing which document to inspect after considering earlier results operates differently. The flexibility can be useful, but it can also increase the consequences of an incorrect choice.
What might a task look like?
Consider a hypothetical meeting-preparation assistant. It clarifies the task, searches permitted files, records missing information and creates a briefing. Each result can influence the next step. Producing that draft does not automatically require permission to send it to other people; external communication is a separate capability.
The example explains why “do everything autonomously” is an incomplete specification. A vague task can lead a system to complete the wrong objective. Researching an event date should not imply authority to send invitations. Reading information and changing the outside world need separate boundaries.
When is simpler automation sufficient?
Not every job benefits from an agent. If the file, rule and output are known in advance, a conventional program may be more predictable. Anthropic recommends starting with the simplest suitable solution and adding complexity only when justified. Agentic approaches become relevant when the necessary steps cannot be fully specified beforehand. [1]
Locate the uncertainty. If only the wording of a paragraph varies, a single model call may suffice. If the system must investigate several sources and choose follow-up research based on results, tools and dynamic planning may help. Both approaches still require an explicit definition of success.
Which operating limits matter?
NIST’s framework encourages evaluation throughout use. KURUM.’s practical starting point is a narrow task and reviewable consequential actions. These suggestions are not a safety certification and must be tested in the application’s own context. [2]
- Grant only the files and tools required.
- Set duration, step and spending limits.
- Require explicit approval for consequential operations.
- Record actions to prevent unintended repetition.
- Define when to stop and hand the task to a person.
How should performance be measured?
Track completed tasks, errors, human corrections, latency and total cost together. One successful demonstration cannot establish reliability across conditions. Evaluation cases should represent the actual workload rather than a selection of easy examples.
Before asking how independent an agent can become, ask which job it performs reliably within which boundaries. The most useful system need not be the one with the broadest permissions. Appropriate authority and clear oversight can matter more than unrestricted autonomy.
Frequently asked questions
Is every chatbot an agent?
No. Chatbot describes a conversational interface; agent behaviour concerns executing tasks and using tools.
Must an agent be fully autonomous?
No. It can operate with human approval and bounded permissions.
Are agents always cheaper?
No. Tool calls, retries, delays and human correction can increase total cost.
Sources
Sources checked: 29 October 2026
