Deep-dive guides on AI agents, agent orchestration, MCP, and developer tooling.
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Four multi-agent frameworks, four incompatible answers to the same question: when one agent hands work to another, what happens to the context? The choice determines what breaks in production.
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.
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.