3 min read
3 min01 · 1 MIN READ
Why plain LLMs hit a wall
Models like GPT and Claude can write, summarise and reason with real fluency. But they only know what they were trained on.
Ask one about your refund policy or the contract you signed on Tuesday and it will guess, often convincingly. Think of a brilliant consultant who has read every business book but never set foot in your office. All three architectures close that gap, each more ambitiously than the last.
02 · 1 MIN READ
RAG: give your AI a reference library
Retrieval-Augmented Generation searches your own data first (documents, knowledge bases, catalogues, CRM records), then answers with that context. It's like handing the consultant a binder of your company's facts before every meeting.
It's the go-to for knowledge search, support chatbots and HR or policy assistants because:
- Answers are tied to real sources, so hallucinations drop sharply
- Update the documents, not the model, when things change
- Role-based access means people only see what they're allowed to
- It's the cheapest and fastest of the three to put into production
The ceiling: RAG can explain your refund process. It can't process the refund.
03 · 1 MIN READ
AI agents: from answering to doing
An AI agent works out which actions reach a goal, then takes them. It calls APIs, queries databases, updates records and sends emails, in a loop of observe, plan, act, evaluate.
A customer says, "I want a refund for order #4829." The agent finds the order, checks eligibility, calculates the amount, reverses the payment, updates the status and sends the confirmation, all in one interaction.
Agents shine in support, sales admin, IT tickets, HR onboarding and invoice processing. The catch: they work inside workflows people designed. They won't notice that a spike in refunds signals a bigger problem.
04 · 1 MIN READ
Agentic AI: the system that plans its own work
Agentic AI adds planning, memory and a degree of autonomy. Give it an objective and it breaks the work into tasks, picks its tools and data, coordinates other agents and adjusts as it learns.
Picture a product launch in a new market: it researches competitors, checks regulations, segments customers from your CRM, drafts campaign briefs and flags risks, with each step shaping the next. No single pre-built workflow covers that
05 · 1 MIN READ
The hype check
Agentic AI is the most powerful option and the hardest to govern. Gartner's numbers are a useful reality check:
- Agentic AI projects Gartner expects to be cancelled by end of 2027
- 40%+
- Vendors, out of thousands, Gartner rates as genuinely agentic
- ~130
- Enterprise apps expected to include task-specific agents by end of 2026
- 40%
“ Many use cases positioned as agentic today don't require agentic implementations. ”
06 · 1 MIN READ
So how do you choose?
Start with the simplest architecture that solves the problem in front of you.
- Start with RAG if the challenge is accurate, grounded answers from your own data. It ships fast and costs least to run.
- Move to AI agents when the system needs to act, not just answer, on well-defined repeatable workflows like refunds, ticket routing or onboarding.
- Explore agentic AI only for genuinely complex, multi-step problems, and only if you have the guardrails, escalation paths and human oversight to support it.
07 · 1 MIN READ
Simple isn't the same as unsophisticated
The most common mistake we see is jumping to the most advanced architecture because it sounds impressive. A well-built RAG system will deliver more value than a poorly governed agentic setup every time.
Build for today's problem. Architect so you can evolve later. Sophistication without reliability isn't an upgrade. It's a liability.
