Memory & Context
Context engineering for AI agents: memory architectures, retrieval, context windows, and the techniques that keep long-running agents coherent.
PillarContext Engineering for AI Agents: Memory, RAG & MCP
Why the context window, not the prompt, is the real bottleneck, and how to engineer memory, retrieval, and MCP around it.
Memory & ContextWhy Memory Bandwidth, Not Compute, Is the LLM Inference Bottleneck
Compute grew ~80x in a decade while bandwidth grew ~17x, and the KV cache turns every decoded token into a memory fetch.
Memory & ContextOn-Device AI's Real Bottleneck Isn't the Chip. It's the Memory
Silicon hit 80 TOPS in 2026, but bandwidth, battery, thermals, and routing logic are what actually gate your local inference deployment.
Memory & ContextLong Context vs RAG: Stop Chunking at the Right Time
Million-token windows changed the default, but retrieval still wins when citations, query volume, and latency matter.
Memory & ContextVector Database Comparison: Speed Is the Trap
Production RAG teams should choose a vector store by operating model, filter shape, and migration triggers, not by a vendor latency chart.
Memory & ContextProduction RAG Chunking Breaks at the Boundary
Semantic chunking helps when boundary errors dominate retrieval failures, but fixed and structure-aware chunks still win when latency, auditability, or corpus shape matters more.
Memory & ContextMemory Poisoning: The Agent Attack That Survives a Reset
OWASP ASI06 corrupts an agent's stored state once and it acts on the lie forever. Here's how the attack works and the defenses that actually hold.
Memory & ContextAI Agent Memory Got Crowded. Here's What Shipped
Four managed agent-memory layers launched in seven weeks. We map who's GA, who's billing, and why the benchmark numbers don't survive an independent harness.
Memory & ContextContext Graphs: The Missing Layer Between Tools and AI Agents
Why flat RAG breaks agentic workflows, what a bi-temporal context graph actually is, and how to build one that holds up in production.
Memory & ContextNeural Memory Abstraction: Context Management for AI Agents
Why the best agent teams are replacing prompt-stuffing and flat RAG with structured, writeable memory layers that combine graphs, vectors, and learned controllers.
Memory & ContextWhat Is MCP? Model Context Protocol Explained for 2026
A plain-language guide to the protocol every major AI vendor now ships, plus a working server you can build in ten minutes.
Memory & ContextModular Context Windows: The Future of AI Agent Reasoning
The race for million-token prompts is over. Production agents won with tiered, modular context instead, and the benchmark evidence now backs them up.
Memory & ContextMulti-Hop Reasoning vs Single-Hop Retrieval for AI Agents
Multi-hop agents win on accuracy, single-hop wins on cost, and the teams that scale are the ones routing between both.
Memory & ContextHybrid Context Storage: Vector + Graph Databases for LLM Agents
A DeepMind proof shows single-vector retrieval is provably lossy. The fix isn't a bigger embedding model, it's pairing vector databases with graph traversal.
Memory & ContextIs the AI Agent Memory Layer the Wrong Abstraction? 2026
The mem0-versus-critics fight isn't about who's right. It's about two evidence classes that never intersect, and you're the one stuck translating.
Memory & ContextContext Rot and the Dumb Zone: Engineering Past 100k Tokens
Bigger context windows didn't fix attention. Past roughly 100k tokens agents get lost in the middle, and the fix is architectural, not bigger.