The Building Blocks of Agentic AI
Every production AI system is assembled from the same core pieces. The difference between a prototype and a production system is understanding how they connect — and where the latent value hides between them.
Each building block links to the others. The real value isn't in any single piece — it's in the connections between them. That's what I help wire up.
Inputs
User prompts, system prompts, programmatic prompts, and the prompt stack that shapes every interaction.
Explore Inputs →Models
Choosing between frontier, open-source, and fine-tuned models. The reasoning engines behind every agent.
Explore Models →Inference
Where and how models run — cloud APIs, self-hosted, on-prem, or edge. The infrastructure that powers everything.
Explore Inference →Context
RAG, memory, system prompts, prompt schemas, and external grounding. Everything that makes generations relevant.
Agents
Agents, pipelines, workflows, and the orchestration layer that coordinates them all.
Explore Agents →MCP
Model Context Protocol — the open standard for connecting models to tools, services, and data sources.
Safety
Guardrails, security harnesses, and human-in-the-loop checkpoints. The brakes on the system.
Explore Safety →Observability
Eval, logging, monitoring, and cost tracking. You can't improve what you can't measure.
Explore Observability →Storage
Conversations, outputs, prompt libraries, data lakes, and knowledge management. Where value accumulates.
How They Connect
The building blocks don't work in isolation. Here are some of the key pairings that unlock real value.
The Feedback Loop
Stored outputs and conversations get mined back into the context store — every generation makes the next one smarter.
Action Layer
Agents decide what to do. MCP gives them the tools to do it — connecting to your data, services, and external APIs.
Trust Layer
Safety sets the boundaries. Observability proves they're being respected. Together they make the system trustworthy.
Grounding
Raw inputs become powerful when enriched with context — RAG, memory, and external grounding turn a prompt into an informed query.
Want help putting the building blocks together?
I specialise in the connections between components — turning the jigsaw into a symphony.