Multi-agent observability: why one trace isn't enough

Multi-agent observability involves correlating agent decisions, handoffs, tool calls, memory activity, retrieval results, identities, and permissions into a causal record of how a system reached an outcome, rather than examining isolated traces or basic metrics such as latency and token use. These systems are difficult to diagnose because model-driven decisions occur at runtime, workflows are probabilistic and can change shape, failures may surface far from their causes, and many errors are silent coordination or design problems that produce plausible but incorrect results. Context can also degrade or disappear during handoffs through hallucinations, clutter, compression, truncation, and limitations in long-context recall, while telemetry often fragments across agents, tools, remote MCP servers, and services without consistent trace propagation. The discussion argues that complete, structured traces improve failure attribution but are insufficient unless they support causal analysis across all components. It presents shared state, modeled partly on blackboard architectures and supported by append-only event logs, as a way to coordinate agents while creating an ordered, replayable audit trail. Redis is presented as one possible real-time shared-state platform, with Redis Iris, streams, search, caching, and agent memory positioned as tools that can support context, persistence, and observability alongside—not instead of—OpenTelemetry instrumentation.

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