Context engineering and memory engineering are two distinct but interconnected disciplines in agentic AI systems. Context engineering governs what information enters a single inference call — covering selective inclusion, structural placement, compression, and token budgeting. Memory engineering governs what persists across calls and sessions — covering write policy design, storage layer selection (vector DBs, K/V stores, relational), retrieval strategy, and maintenance routines like TTL expiry and deduplication. The two disciplines meet at the retrieval boundary, where two common failure modes arise: injecting retrieved memories without a context budget, and placing retrieved content in low-attention regions of the context window. Treating retrieval as the first step in context construction — with budget constraints and placement optimization — is what makes agentic systems reliable across real workloads.
Table of contents
IntroductionAn Overview of Context and Memory EngineeringContext Engineering: Assembling the Optimal Context WindowMemory Engineering: Designing Persistent AI Memory SystemsThe Retrieval Boundary: Connecting Memory and Context EngineeringSummary126 Impressions1 Comment