Deep-dive guides on AI agents, agent orchestration, models, and developer tooling.
4 posts found
Most agent security advice targets prompt injection at the wrong layer. The real fix is architectural: separate untrusted tool output from privileged context, scope tool capabilities narrowly, and gate side-effecting actions behind confirmation.
Benchmark leaderboards measure task completion under lab conditions, not the compounding step failures that sink agents in production. Here is the math, and a blueprint for an eval harness that actually predicts reliability.
Vector similarity search answers what text is topically related — but long-running agents need to know what is true right now. Conflating the two is why agents keep resurrecting overturned decisions.
A wire-level look at the Model Context Protocol — capability negotiation, tool discovery, transport tradeoffs, and the context-budget mistakes that quietly degrade agent reliability.