THE KEY ANSWER
A workflow is effective when the sequence of steps and rules are known. An agent is useful when the path to the goal depends on the results of previous actions. Choose the level of autonomy based on the task, available safeguards, and the cost of errors.
The difference lies in control over the process
In a controlled workflow, the application determines the sequence: retrieve the document, extract fields, check completeness, display the result. The model may perform a single step but does not independently decide on the entire path. In an agentic approach, it selects tools and subsequent actions based on what it has established earlier.
This distinction helps in discussing responsibility. Saying “We want an agent” does not clarify whether the system should only prepare a recommendation or also modify data. In the specification, describe the goal, permitted operations, and stopping conditions separately. The technology name should be the result of this discussion, not a substitute for its content.
Context and references: Anthropic: Building effective agents
Example: What to do with a quote request?
If every request requires checking the same fields and applying the price list, start with a workflow. The model organizes free text, and rules check for completeness. Sending a request for missing data may require approval. This makes it easy to identify the stage where the process stalled.
An agent may be justified when preparing a response requires finding information in several different sources, and the search order depends on the problem. It is still worth limiting the output to a draft. Independently researching sources and independently making commercial commitments are two different product decisions. They should not be enabled by a single switch.
Autonomy has a cost that must be visible
Subsequent tool calls extend the task duration and increase costs. A failed attempt may trigger the next one, so you need limits on steps, time, and expenditure. Show the user what was done and why the system stopped. An indefinite “working on it” is not a useful product state.
Test for missing data, unavailable systems, and conflicting results. Check whether the agent can complete the task without feigning success. Count correctly completed cases, not the number of operations performed. If the solution requires constant supervision, a simpler workflow with a few well-defined exceptions may provide greater value.
How to expand the scope of responsibility?
Start with reading and recommendations. Then add limited actions whose effects can be verified and reversed. For external operations, prepare a preview of the specific change, the scope of consent, and a log of the result. Permissions are enforced by the application and the target system, not by a polite instruction to the model.
Expanding autonomy should be based on the measurement of a specific class of tasks. Good results in document search do not automatically justify the right to delete records. Consider each operation separately. During implementation, retain the ability to switch to draft mode to limit the impact of unexpected behavioral changes.
WHERE TO START
Bring this into your project.
- Describe who selects the next steps.
- Separate reading, recommendation, and data modification.
- Set limits for time, cost, and number of actions.
- Expand autonomy based on the results of a specific operation.
Choose one thing your process is missing today. It's a useful topic for your first conversation with the team.
QUESTIONS AND ANSWERS
Frequently asked questions.
Is an agent always a more advanced solution?
It is more autonomous, but not always more useful. A well-defined process may work faster and more clearly as a workflow. The choice should be driven by the unpredictability of the task.
Can both approaches be combined?
Yes. A controlled process can invoke an agent only to gather information, then validate the result and pass it for acceptance. This allows maintaining flexibility in a selected part of the process.
Sources and context
- Anthropic: Building effective agents ↗
Anthropic distinguishes between workflows with paths defined in code and agents that dynamically select actions. The source article dates from 2024; we use this distinction, not the list of tools from that time.
Prepared by the ALGOV team. Current as of September 8, 2026. Examples describe possible scenarios, not results from client projects. How we create our guides.
