AI & Intelligence
The discipline of delivering machine intelligence as enterprise systems — retrieval, generation, agents, and context under production constraints.
What this means here
AI & Intelligence is the practice of making machine reasoning useful inside real organisations: not isolated model calls, but systems that retrieve context, generate responses, take action, and remain accountable.
It spans the full intelligence path — grounding truth before generation, shaping prompts and context, orchestrating agents and tools, and routing inference with explicit tradeoffs for latency, cost, and quality.
It is not chatbot demos, model shopping, or autonomy without boundaries. It is systems design where the model proposes and the surrounding architecture decides: what data is in scope, what tools may run, what gets logged, and what requires human approval.
In enterprise and regulated settings, intelligence only matters if it is repeatable, explainable, and safe to operate. That requires treating AI as architecture — pipelines, runtimes, and control points — not as a feature bolted onto legacy applications.
What it should cover
Reach for this domain when the question is how intelligence is produced and delivered — from context and retrieval through generation, tool use, and governed outcomes.
LLM & inference patterns
Model selection, routing, grounding, and production constraints — latency, cost, and quality tradeoffs made explicit.
RAG & context engineering
Retrieval pipelines, context engines, memory models, and how truth enters the system before generation.
Agents & orchestration
Tool use, execution flows, human checkpoints, and autonomy boundaries in enterprise workflows.
MCP & tool integration
Governed capability surfaces — how agents reach enterprise systems without bypassing policy.
Production AI adoption
Rollout patterns, eval harnesses, and the path from pilot to platform — not proof-of-concept theatre.
On this site
Content is organised by section, filtered by domain. Start anywhere below — all paths lead back to the same practice model.
Standing up governed GenAI or agent capability in a regulated environment? This is the domain I advise and build in public.
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