Agentic AI vs RAG vs Workflows: How to Choose

Use a fixed workflow when the steps are known in advance, RAG when the task is answering from knowledge that changes, and an agent only when the path cannot be written beforehand. Pick the least autonomous option that solves the problem, then earn more autonomy through evaluation.

What is the difference between agentic AI, RAG and workflows?

They answer three different questions. A workflow asks who decides the steps, and the answer is people, in advance. RAG asks where the knowledge comes from, and the answer is a retrieval step over your documents. An agent asks what to do next, and the answer is a model deciding at run time.

Most confusion comes from reading them as rungs of a maturity ladder. They are not. They are tools with different failure modes, and the best one is usually the least autonomous one that works.

Fixed workflow
RAG assistant
Single agent
Multi-agent system

Autonomy rises from left to right, and so does the evaluation burden. Move one step only when the simpler option has been measured and falls short.

How do you define each approach precisely?

Vague definitions produce vague architecture, so here are the ones I use with teams. The axis that matters is autonomy: who chooses the path.

Fixed workflow: a sequence of steps written by people. A model may be called inside a step, to classify or draft, but it never chooses the next step. Behaviour is predictable and can be tested step by step.
RAG assistant: a pipeline that retrieves passages from a document collection and asks a model to answer from them, ideally with citations. The path is still fixed. What varies is the knowledge, which is why it fits content that changes.
Single agent: a model that works in a loop, decides which tool to call next, reads the result and decides again until it judges the task done. The path is chosen at run time.
Multi-agent system: several agents with specialised roles, coordinated by an orchestrator that plans, delegates and checks the work. It adds capability, and it adds coordination failures.

How do you choose between a workflow, RAG and an agent?

Retrieval is often a component rather than a rival: an agent can call a retrieval tool, and a workflow can contain a RAG step. The table below is my own rule of thumb, not a standard. Read each row as a question to ask before you pick an architecture.

Criterion	Fixed workflow	RAG assistant	Agent (single or multi)
Task predictability	High: steps known in advance	High: one retrieve-then-answer path	Low: the path depends on what is found
Need for fresh knowledge	Low, data is passed in as input	High: answers come from a changing corpus	Medium to high, usually through retrieval tools
Number of steps	Few or many, but fixed	Few and fixed	Many and variable
Tool use	Scripted calls	Retrieval only	Dynamic choice of tools
Cost of error	Steps can be gated, so risk is controllable	Medium: answers can be checked against citations	Needs approval gates when the cost is high
Auditability	Easiest: the path is the code	Good: sources are cited	Hardest: needs recorded traces
Evaluation difficulty	Lowest: test each step	Moderate: score retrieval and answers separately	Highest: score paths and outcomes

What are the rules for where to start?

These rules come from a single principle: every step up in autonomy buys flexibility and costs predictability. Pay that price only when the problem demands it.

Start with a workflow if you can draw the steps on a whiteboard. Most business processes can.
Add RAG when the missing ingredient is knowledge, not reasoning. Fix retrieval quality before you touch the model.
Move to a single agent only when the number of branches makes the workflow unmaintainable, or when the next step truly depends on what the previous one returned.
Go multi-agent only when one agent’s context or toolset becomes too large to govern, and when you can evaluate each specialist on its own.
Put a human approval gate before any step that writes to an external system, whatever the approach.
Never raise autonomy without an evaluation showing that the new version beats the previous one on the same scenarios.

What did the DILA platform show about RAG and agentic workflows?

On the sovereign AI platform I worked on for DILA, the services of the Prime Minister, the starting problem was duplication. Every AI application had to rebuild document ingestion, embeddings, retrieval, orchestration, security, evaluation and observability. We moved from building each application separately to assembling RAG applications from reusable, governed components.

Agentic workflow prototyping then ran on the same foundations. Agentic applications could build on existing orchestration patterns and tool integrations instead of starting from nothing, and the case page records a 50% reduction in implementation time for prototyping them.

The lesson I draw is architectural. The agent was not a separate stack. It sat on the same governed components as the RAG applications, so autonomy was an addition on a platform and not a new project with its own security and evaluation story.

When is a multi-agent system justified? The NOVA example

NOVA is a multi-agent system, and I built it as one for a reason. Its job is to turn an intent into a plan and an editable deliverable, and the path genuinely differs from one request to the next. An orchestrator frames the request, delegates to specialist agents for product, project, design and engineering, and a validation agent checks each deliverable. Context comes from ORBIT with the user’s permissions, and FORGE evaluates the runs.

My own reading is that the autonomy sits only where the path varies. The surrounding parts, such as versioned skills, governed retrieval and structured deliverables, behave much more like workflows and RAG. I would not have justified the orchestration for a task whose steps I could list in advance.

A caution on status. NOVA, ORBIT and FORGE are public repositories in beta, and I describe their design, not a production guarantee. What I can state as a fact is that NOVA Suite is used by 800 employees at Devoteam.

How do you earn autonomy with evaluation?

Evaluation is the gate between levels. Before moving from one approach to the next, define scenarios, score the current version and require the more autonomous candidate to beat it on the same cases.

For RAG, DILA used four measures: faithfulness, answer relevancy, context precision and context recall. For agents, the FORGE README describes scenarios, deterministic rules, LLM judges, human review and baseline versus candidate experiments. The principle is the same: a score must trace back to a criterion and a step.

Include the cases where the system must refuse or escalate, not only the ones where it should succeed. And measure cost, because autonomy tends to add model calls. At DILA, pipeline optimisation and reuse brought the cost per AI request down by 30%.

What did I measure and see?

These figures come from the DILA case study and concern the platform approach: reusable components, evaluation and governed orchestration. They are not a comparison between workflows, RAG and agents, and I do not have one to offer.

Measure	Result
RAG application delivery time	-60% (10 weeks to 4)
Common technical capabilities reused	75%
Agentic workflow prototyping implementation time	-50%
Cost per AI request	-30%
RAG faithfulness	0.94
Answer relevancy	0.91
Context precision / recall	0.89 / 0.92
Priority RAG scenarios covered by evaluation	85%

FREQUENTLY ASKED QUESTIONS

What is the difference between agentic AI and RAG?

RAG retrieves passages from your documents and answers from them along a fixed path. An agent decides at run time which tools to call and what to do next. RAG is about where knowledge comes from, agency is about who chooses the path, and an agent can use RAG as one of its tools.

When should I use a workflow instead of an AI agent?

Use a workflow when you can write the steps in advance, when errors are costly and when you need a clear audit trail. A model can still classify or draft inside a step. If the path never changes, autonomy only adds risk and makes evaluation harder.

Is RAG a kind of agent?

Not by itself. A basic RAG assistant follows one retrieve-then-answer path that people designed. It becomes agentic when a model decides whether to retrieve, what to retrieve and what to do with the result, in a loop. The boundary is who controls the path.

Do I need a multi-agent system?

Rarely at the start. Consider it when one agent’s context or toolset is too large to govern and each specialist can be evaluated on its own. NOVA uses an orchestrator with specialist agents because its tasks vary a lot, but a simpler design is usually the better first step.

How do I know my system is ready for more autonomy?

When an evaluation says so. Define scenarios, including cases where the system should refuse or escalate, score the current version and test the more autonomous candidate against it. If it does not clearly beat the baseline, keep the simpler option and keep human approval on risky steps.