The Moving Pieces
Every production AI system is made of the same building blocks. Here's how they fit together — and where I can help.
The slide we wheel out to make it look like we know what we're doing
It's a Cycle, Not a Pipeline
At first glance, the diagram looks like a waterfall — prompts go in at the top, outputs come out through the agents. But the real power of agentic AI is that every layer feeds back into every other layer. Outputs get stored, stored data gets mined for context, context improves the next generation, better generations produce richer outputs. Prompts get analysed and refined into a library. Agent actions flow through integrations that enrich the data the agents will draw on next time. It's not a straight line — it's a flywheel. The more you use it, the smarter and more valuable it becomes.
Layer by Layer
Models
The reasoning engines behind every agent. Commercial frontier models (Claude, GPT, Gemini) offer cutting-edge capability via API. Open-source models (Llama, Mistral, Qwen, DeepSeek) give you full control. Fine-tuned models are specialized on your data for domain-specific tasks.
Inference
Where and how models run. Cloud APIs are the simplest path — zero infrastructure. Self-hosted gives you control over cost and latency. On-prem is required for air-gapped or regulated environments. Edge inference runs lightweight models directly on devices.
Agents
The things that do things. An orchestration layer coordinates everything — routing tasks, handling failures, managing state. Underneath: agents reason and act autonomously, pipelines process data in defined steps, and workflows trigger on events.
MCP
Model Context Protocol — the open standard for connecting AI models to external tools and services. MCP servers bridge agents to your databases, APIs, and third-party integrations. Not typically used for RAG or memory, which are integrated directly.
Data Sources
Where your agents get their information. Your internal data, external APIs, web search, persistent memory, and RAG over your knowledge base.
Frontends
Where users meet your agents. Chat interfaces, Slack and Telegram bots, web dashboards, and custom UIs tailored to your workflows.
Safety
Guardrails define what agents can and can't do. Security harnesses protect against prompt injection, data leakage, and unauthorized actions.
Observability
You can't improve what you can't measure. Eval frameworks test output quality, logging captures every decision, and monitoring alerts you when things go sideways.
Latent Value
Most teams build the forward path — prompt in, output out — and stop there. But the real compounding value lives in the feedback loops most people never wire up.
Mine your existing data for context
You already have years of business data sitting in production databases, CRMs, and document stores. Running extraction and embedding pipelines over this data builds a context store that makes every future generation smarter, more grounded, and more relevant to your specific business. Most teams start from zero when they could start from ten years of institutional knowledge.
Mine your stored prompts
Every prompt your users send is a signal — what they're asking for, how they phrase it, where the model struggles, what patterns recur. Systematically analysing stored prompts reveals which system prompts work, which fail, and where the gaps are. This feeds directly into a prompt library that improves over time instead of staying static.
Capture outputs to build a knowledge base
Every output your agents generate — research reports, summaries, analyses, structured data — is potential institutional knowledge. Without a capture pipeline, this value evaporates after delivery. With one, every generation enriches a wiki or knowledge management system that compounds over time, reducing repeat work and building organisational memory.
These aren't advanced features — they're the difference between an AI system that stays flat and one that gets better every week. The diagram above shows where these loops connect. I help wire them up.
Deployment Patterns
The same building blocks get assembled differently depending on your security requirements, data sensitivity, and infrastructure. Each pattern below includes a full architecture diagram with security annotations.
On-Premise Deployment
Self-hosted open-source models on your infrastructure. Full data sovereignty, full responsibility. Security-annotated architecture.
SaaS / API Deployment
Commercial model APIs with managed inference. Fast to deploy, but data crosses trust boundaries. Security-annotated architecture.
Inference Gateway
Route between multiple models through a gateway like OpenRouter or self-hosted LiteLLM. Flexibility, fallbacks, and cost control.
Air-Gapped Deployment
Fully isolated infrastructure with no internet connectivity. Maximum security for classified, regulated, or high-sensitivity environments.
Want help putting the pieces together?
I build the full stack — from model selection through to production deployment.