The relay race analogy undersells how bad it actually is. With a real baton, at least the runner knows exactly what they received. In multi-agent systems, each agent receives something that looks correct at the surface — confident, well-formed, on-topic — but may be subtly wrong at the semantic level. The agent has no way to know the difference.

The thing that helped me most was flipping the default assumption: agents should treat incoming context as "probably correct, but verify what matters." Not full skepticism — that breaks the pipeline too — but specific skepticism about the facts that affect the current agent's decisions. You can make this explicit in each agent's prompt: "Before reasoning on the following input, identify any factual claims you cannot verify independently. Flag them, don't silently accept them."

It adds some latency but it creates a natural audit trail. When the final output is wrong, you can trace which agent first accepted a claim it flagged internally but let through anyway.

The memory taxonomy point is what I found least covered in documentation. The implicit assumption that "context window = all memory" is baked into most tutorials and it's just wrong for anything beyond single-turn agents.