The Context Window Editors

AI agents need permission controls designed for rapid, autonomous action, not static human roles.

Attackers exploit RAG systems by crafting queries that leak sensitive knowledge base contents.

Attackers exploit tool-connected agents through hidden instructions in retrieved data.

Clever prefix design and TTL timing determine whether agents actually capture caching savings.

Domain expertise beats leaderboard rankings when selecting embeddings for specialized text.

Placing better documents at the top of the context window dramatically lifts RAG answer quality.

Filter metadata before vector search runs to prevent irrelevant results.

Two retrieval methods catch what the other misses, boosting accuracy over either alone.

Match the workload to the database, not the benchmark, to build RAG systems that actually work.

Three cache layers demand different invalidation strategies to keep costs down.

Chunking matters as much as your embedding model—and most teams optimize the wrong layer.

Choose stateless for short tasks, stateful for long conversations.

Context isolation prevents token degradation as AI systems handle longer, more complex tasks.

Teams must evaluate agent trajectories and tool calls, not just final outputs.

LLM agents fail predictably at tool selection, not randomly.

Persistent memory layers, not bigger context windows, solve agent degradation.

Shared context stores are the load-bearing component that makes or breaks multi-agent coordination.

ReAct adapts step-by-step while Plan-and-Execute locks in the full sequence upfront.

Prevent cascading failures by placing checkpoints where mistakes can't be quietly undone.

Stdio works locally but scales to zero; Streamable HTTP handles remote teams and audit trails.

Servers can borrow the client's model, but the spec is already reworking how.

How MCP servers went from manual config chaos to federated discovery infrastructure.

Misclassifying Tools and Resources wastes tokens on every agent turn and silently tanks performance.

OAuth 2.1 becomes mandatory as MCP matures past its early prototype phase.

MCP sessions waste tokens on tool definitions before users even ask questions.

Master the distinction between tools, resources, and prompts before building your server.

A vendor-neutral standard that lets AI models connect to any tool without custom code.