There is no agreement in those reports. The summary invented one by merging words that sound related. Before accepting either conclusion, keep each branch's hypothesis, observation, timestamp, source revision, query window, and confidence or uncertainty separate. Also keep the actual tool result IDs, not only the branch's prose. The coordinator should know that one dashboard may not include the post-deploy interval, while the trace is one sample of that interval. Neither agent's title or eloquence makes the evidence stronger.

I would assemble a small comparison for the next step: what each branch claims, which observations support it, which observations contradict it, and what is still missing. Pin a common incident timeline: deploy time, first bad request, cache metrics at the relevant window, downstream error rate, and trace versions. If the branches queried different environments or tenant slices, say so. The next tool call should discriminate between hypotheses, perhaps compare the same request path before and after the deploy or check whether cache misses precede downstream timeouts. A third agent repeating the same stale dashboard is not independent confirmation.

The important distinction from a citation race is that both branches can cite exactly the results they saw and still reason incorrectly together. A branch report is an interpretation. The shared context needs evidence with provenance and an explicit unresolved contradiction. OpenAI's agent tracing documentation shows model calls, tools, and handoffs as separate trace elements. That kind of trace helps reconstruct what each branch observed, but the application must still align incident time and decide what evidence establishes causality.

Would I roll back immediately? That depends on impact and reversibility. If the deploy is the only recent change and rollback is low risk, an operator may choose it while investigation continues. That is an incident mitigation decision, not proof that the cache hypothesis won. If rollback is costly or can corrupt in-flight state, ask for one more targeted measurement or take a safer containment action. The coordinator should present the evidence and uncertainty to the authorized operator rather than turn an invented consensus into an automatic production action.

Suppose both sources are current and the dashboard shows cache misses rising exactly when the downstream service slows. Correlation still leaves direction open. A trace can reveal whether a cache miss caused the downstream call or the downstream timeout caused retries that amplified misses. Check request-level order and a counterfactual intervention where feasible, such as isolating a canary or reverting only a cache configuration. Do not make the model vote on a causal arrow from two aggregate graphs.

I would test the context assembler with deliberately conflicting branch reports and a hidden source change between them. The success condition is not that the coordinator always picks one. It should retain the disagreement, ask a useful discriminating question, and keep any proposed action inside the operator's actual approval boundary.