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Practical thinking on AI agents and AI visibility
Plain-English guides on context engineering, harness engineering, agent loops, MCP, GEO and RAG, from the team that builds them.
Context engineering: the discipline behind reliable AI agents
Why what you put in the context window matters more than how cleverly you word a prompt, and the practical techniques that keep agents accurate over long tasks.
9 min read →Harness EngineeringHarness engineering: build the system around the model, not just the prompt
An AI agent is a model plus a harness. Here is what the harness contains, why it determines reliability, and how to design one for production.
9 min read →AI AgentsThe agent loop: how AI agents plan, act and verify
The simple pattern at the heart of every AI agent, why loops fail in practice, and the controls that keep them safe, bounded and useful.
8 min read →MCPModel Context Protocol (MCP) explained for enterprises
What MCP is, how it connects AI agents to tools and data through one open standard, and what enterprises should consider around security and governance.
9 min read →AI GEO & SEOGenerative Engine Optimization (GEO): a practical guide
How AI assistants decide who to recommend, how GEO differs from SEO, and a practical checklist for earning citations in AI answers.
10 min read →LLM & RAGRAG, fine-tuning or long context? How to choose
Three ways to give a model your company's knowledge. What each is good at, where each fails, and a simple decision guide.
8 min read →AI AgentsAgent evaluation: how to know your AI agent actually works
Why agents are hard to test, what to measure, and how to build an evaluation suite that catches regressions before your customers do.
9 min read →AI AgentsMulti-agent systems: when more agents help, and when they do not
Orchestrators, specialists, handoffs and critics: the main multi-agent patterns, their real costs, and a practical rule for deciding if you need more than one agent.
9 min read →AI SecurityPrompt injection and AI security: protecting agents that can act
Why prompt injection is the defining security risk for AI agents, how attacks work, and a layered defence that assumes the model can be fooled.
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