Practical context engineering for AI agents: how to structure, deliver, and manage the context that determines whether AI systems actually work.
Research-backed articles on context architecture, retrieval, agent security, and making AI tools more effective. Written by Jitpal Kocher.
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Up to 86.7% of multi-agent AI runs fail. Most failures trace back to how agents share context, not the agents themselves.
Seven context engineering techniques used in production AI systems, with implementation patterns, research backing, and guidance on when each one works.
ETH Zurich found AI-generated context files hurt agent performance by 3%. The problem is structure, not volume. Here's what the research says.
New research analyzed 3,282 MCP bug reports. The patterns reveal a context delivery problem, not a protocol problem. Here's what the research shows.
A context window is the total text an AI model can process at once. Learn how they work, why size isn't everything, and what actually affects performance.
88% of organizations report AI agent security incidents. The root cause is a context engineering failure: agents get all-or-nothing access instead of scoped context.
GPT-5.2 hallucinates at 10.8%, o3-pro at 23.3%. The fix has less to do with better models and more to do with better context engineering.
Claude now imports ChatGPT memories, but conversation history, files, and team context don't transfer. Here's what actually moves and what doesn't.
Prompt engineering is a dead end. Context engineering is the discipline replacing it. Here's what it is, why it matters, and how to apply it.
94% of IT leaders fear vendor lock-in. Every AI tool traps your context in its own silo. Here's why your AI doesn't remember you, and what's changing.