AI & Agentic Systems:
Technical Deep Dives
A deep-dive series on the technical foundations of modern AI — agentic system design, context engineering, evaluation infrastructure, and emerging architectures. Articles in this series are written for practitioners who want substance over surface.
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LLM Caching Strategies: Faster Responses, Lower Costs—Without Stale Answers
What caching means in AI, how prompt and semantic caching differ, where to use each, and how they reduce repeated computation, token costs and response time—with diagrams and trade-offs.
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Harness Engineering: Building the Test Infrastructure Your AI Agents Actually Need
The discipline of constructing rigorous, repeatable test infrastructure for AI systems — covering golden datasets, LLM-as-judge evaluation, regression suites, and CI/CD evaluation gates.
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Agentic AI Architecture: A Complete Layer-by-Layer Technical Guide
A comprehensive technical deep-dive into every layer of a production-grade agentic AI system — orchestration, specialised agents, memory, tools, observability, reliability, governance, and infrastructure.
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Vectorless RAG: When LLM Reasoning Replaces Vector Search
How the PageIndex architecture replaces vector similarity with LLM reasoning to plan retrieval — and why it outperforms traditional RAG for structured, multi-hop, and auditable enterprise queries.
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Memory in AI Agents: Architecture, Risks and Enterprise Management
A practical guide to working, semantic, episodic and procedural memory—covering production architecture, poisoning, privacy, stale facts, lifecycle controls and enterprise governance.
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GraphRAG: Design, Advantages, Tradeoffs and When to Use It
A practical guide to GraphRAG design, industry adoption and platform selection—comparing Neo4j, managed cloud graph services, distributed databases and semantic knowledge graph platforms.
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Advanced RAG: Building Retrieval Systems That Actually Work
A practitioner's deep-dive into the full RAG pipeline — from chunking strategies and metadata design to query optimisation, reranking, and output validation.
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Prompt Engineering for Enterprise AI: From Vague Inputs to High-Impact Outputs
A practitioner's guide to the five types of prompting, the Role+Task+Context+Format+Constraints formula, and why prompt quality is governance in agentic AI systems.
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API vs MCP vs A2A: The Three Layers of the Modern AI Agent Stack
A clear, technically grounded breakdown of API vs MCP vs A2A — what each one actually standardizes, how industry is using MCP and A2A across HR, customer support, finance, and dev tools, plus a decision framework and the security risks both protocols share.
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Agentic AI concepts broken down without the jargon. Watch to grasp the architecture, then read the articles for full technical depth.
Governance · now playing
How to Govern AI Agents at Scale | The Architecture of Controlled Autonomy
