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Published2 min read

From RAG to agents: choosing the right AI automation pattern

Not every problem needs an autonomous agent. A practical guide to picking the simplest AI pattern that solves your problem.

  • AI automation
  • RAG
  • Agents

The fastest way to burn a budget on AI is to reach for the most complex pattern first. A multi-agent system is impressive in a demo and fragile in production. Most real problems are solved by something far simpler — if you choose deliberately.

Here is how we think about the ladder of patterns, from simplest to most capable.

1. A single prompt

If the task is "transform this input into that output" — summarise, classify, extract, rewrite — a single well-engineered prompt is often the entire solution. No retrieval, no tools, no loop. Start here and only climb if you must.

2. Retrieval-augmented generation (RAG)

When the model needs knowledge it was not trained on — your docs, your policies, your data — give it that context at query time instead of fine-tuning. RAG is the workhorse of practical AI: cheaper, more current, and far easier to audit because you can see exactly which sources informed each answer.

The hard part of RAG is rarely the model. It is the retrieval: clean chunks, good embeddings, and ruthless evaluation of whether the right context actually surfaced.

3. Tools and function calling

When the task requires acting — querying a database, calling an API, creating a ticket — let the model call typed functions you control. The model decides what to do; your code decides how, with validation at every boundary.

4. Agents — only when the path is unknown

An agent is worth its complexity when the steps cannot be known in advance and the system must plan, act, observe and adapt. That power comes with cost: agents are harder to test, harder to bound, and harder to trust. Reserve them for genuinely open-ended problems, and always keep a human in the loop where the stakes are high.

The best AI architecture is the simplest one that solves the problem — and not one rung higher.

In short

Climb the ladder deliberately: prompt, then RAG, then tools, then agents. Each rung adds capability and cost. The teams that win with AI automation are not the ones using the most advanced pattern — they are the ones using the right one.