These approaches address different parts of a system. They are not interchangeable products, and a useful application may combine them with ordinary software rules.
Before selecting a technique, identify what is currently failing: access to information, output format, task execution or the surrounding application.
When the problem is access to current information
A retrieval-based approach searches an approved collection and supplies relevant context to a model. This can be useful for document-grounded questions, policies or product knowledge.
The work includes preparing sources, defining permissions, keeping the collection current and evaluating the answer against retrieved evidence. Adding retrieval does not by itself prove that an answer is correct or that access boundaries are enforced.
When the problem is a repeated behaviour or output pattern
Begin with clear instructions, examples and output validation. If those are insufficient, adaptation or fine-tuning may be worth evaluating against an agreed dataset.
The decision should be based on a measurable improvement for the task, not on the assumption that a custom-trained model is inherently a better business solution. Keep training and evaluation examples separate when assessing generalisation.
Fine-tuning is not a substitute for a live source of changing facts or for application-level access control.
When the problem is structured information
The useful design may be a schema, extraction step, validation rules and a review screen. A conversational assistant is not necessary simply because the input is a document.
Define which fields can be inferred, which require direct evidence and which must remain empty when unsupported. The destination's requirements determine the output, not the model's preferred wording.
When the problem is a sequence of actions
Map the workflow and distinguish fixed transitions from steps requiring interpretation. A conventional job queue and a few model calls may be more appropriate than an open-ended agent.
Where an agent chooses tools or proposes actions, define its permissions, stopping conditions, approval requirements and recovery behaviour. The system still needs reliable software around the model.
Compare approaches on the same task
Create representative examples, define acceptance and include relevant failures. Compare quality, review effort, latency, operating cost and implementation complexity.
Do not give a more complicated architecture a free pass because it has more components. Equally, do not reject necessary controls solely because a simpler demo is faster on one clean example.
A practical starting question
Complete this sentence: “The user needs to turn this input into this accepted result, using these sources and permissions.”
That sentence is a better foundation for architecture than “we need RAG” or “we need an autonomous agent”.
Discuss the architecture · Knowledge assistants · Workflow automation