Context Engineering: Designing AI That Understands, Remembers, Acts, and Learns
English | October 13, 2026 | ASIN: B0HMZBWH5Q | 354 Pages | Epub | 8.31 MB
Reliable software built on large language models fails in a new way: context drift. Demos behave differently from production, confident answers come from broken data, and no amount of prompting discipline fixes it - because the problem is architectural, not textual.Context Engineering is the engineering manual for this failure mode. Dr Alexander Mikhalev lays out a complete discipline for designing AI systems that understand, remember, act, and learn: knowledge graphs and deterministic search as grounding gates, token-budget architecture and compression strategy, skill architectures and project-wide rules, validation harnesses with typed decision models, and governance patterns for multi-agent systems in the enterprise.Written for senior developers, engineering managers, and architects who have shipped at least one LLM-backed system and watched it behave differently in production than in the demo. Every chapter ends with its sources; the author reports measurements from his own tooling with stated limitations. The text, diagram sources, and build pipeline are publicly available, so every claim can be inspected, not just asserted.
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