Deep-dive guides on AI agents, agent orchestration, MCP, and developer tooling.
4 posts found
In agent systems, instructions live across four surfaces — system prompt, tool schemas, tool results, and few-shot text — not one. Most prompt debugging still only looks at the first.
Long-running agents rarely fail because they run out of context window — they fail because nobody designed what happens to attention quality once the transcript outgrows what the model can usefully weigh. Here is how tiered compaction, tool-output pruning, and sub-agent isolation actually work.
The three dominant multi-agent orchestration topologies each fail in a different, predictable way once you move past the demo — here is how to pick one based on where your task actually breaks, not which pattern sounds more sophisticated.
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.