In a three-stage pipeline — research agent → synthesis agent → writing agent — the synthesis agent needs access to the raw findings produced by the research agent. Which state-sharing pattern is most appropriate?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Think of a relay race — the baton has to be handed off cleanly, not carried by one runner while the next one jogs alongside them. On the exam: durable named artifacts are the correct inter-agent state handoff mechanism; passing large outputs through prompt prefixes or environment variables is fragile and doesn't scale.
Full explanation below image
Full Explanation
In a sequential multi-agent pipeline, each stage must receive the previous stage's output in a reliable, inspectable, and scalable way. Writing findings to a named durable artifact (option B) achieves all three: the research agent writes once, the synthesis agent reads at its own pace, the data persists through failures, and the artifact serves as an audit trail. This is the standard pattern for inter-agent state handoff.
Option A is tempting because passing output as a prompt prefix seems direct, but it has critical limitations: context windows have size limits, the data is ephemeral once the session ends, and it cannot be inspected or replayed by the orchestrator. For anything beyond trivial data volumes, this approach breaks down.
Option C is incorrect because re-running the research agent wastes compute, may produce different results (non-deterministic agents), and creates a hard coupling between stages that defeats the purpose of a modular pipeline. It also doubles the cost and latency of the research phase.
Option D is incorrect because environment variables are not designed for structured, potentially large datasets. They have system-level size limits (typically a few megabytes total), are not durable, are not queryable, and expose data in a low-visibility format that is difficult to audit. Local coordinator variables are in-memory and lost on restart.