Observability correlates traces, metrics, logs, prompts, models, graph versions, and user outcomes. It should answer which path ran and why without collecting unnecessary content.

Graph Observability Connects Runs to Design

An incident process detects harm, stops or limits execution, preserves evidence, notifies responsible parties, repairs effects, and updates graph controls. Model-output deletion alone is not remediation.

Agent Incidents Need Containment and Learning

Chaos testing injects bounded dependency, delay, duplicate, corruption, and permission failures to verify recovery. Experiments need hypotheses, safety limits, observability, and abort controls.

Chaos Tests Exercise Designed Failure Paths

A production run should bind a graph topology, node code, prompts, schemas, models, policies, and resource versions. Without that bundle, rollbacks and comparisons are ambiguous.

Deploy Graphs as Versioned Executable Artifacts

An evaluation corpus should cover common, rare, ambiguous, adversarial, and protected-user situations across important graph paths. A random sample of easy successes cannot establish system reliability.

Evaluation Corpora Must Represent Real Routes

An interpretable agent graph lets investigators trace which nodes, inputs, models, rules, and tools influenced an outcome. A neat diagram is not enough if runtime decisions remain opaque.

Graph Interpretability Connects Structure, Decisions, and Evidence

In this situation—diagnosing a latency spike confined to one retrieval branch—which choice best applies “Graph Observability Connects Runs to Design”?

Map collection, inference, storage, retrieval, sharing, logging, and deletion across graph edges. Threats include overcollection, linkage, memorization, unauthorized inference, and retention drift.

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