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We've all heard about prompt engineering. But now with the emergence of context engineering you may be scratching your head about what the difference is.

The reality is that to build reliable agents, we need to use all tools in our context toolbox to ensure they provide accurate responses in the correct format.

Let's dive into the world of context engineering, and understand the elements of context that we can tune and tweak to build more reliable AI agents and associated systems.

NDC Conferences

A conference talk from NDC London 2026 covering context engineering for building stateful, intelligent LLM agents. Topics include why LLMs hallucinate, the components of context engineering (user prompts, system prompts, RAG, memory, tools, structured output), and practical prompting strategies like chain-of-thought, self-consistency, meta-prompting, and ReAct. The talk includes live demos of a TypeScript travel chatbot using AI SDK, Elasticsearch for vector search and memory, tool calling with Zod schemas, and memory management via summarization and temporal pruning. Also covers agent-to-agent protocols (A2A), Model Context Protocol (MCP), observability with OpenLIT, and RAG-MCP for optimizing tool selection.

Crafting Intelligent Agents with Context Engineering - Carly Richmond - NDC London 2026