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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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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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.
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Agentic AI Architecture Explained
