Multi-agent systems lack mechanisms to detect failures at the composition level when all individual components report local correctness—deadlocks, metric decay, and pipeline collapses remain invisible until they cause operational harm. Current observability tooling is component-centric and cannot surface emergent system-level dysfunction. This gap creates a critical blind spot in production agent deployments where individual health signals are meaningless proxies for global function.

Multi-agent systems fail silently at the composition level—deadlocks, cascading metric decay, and pipeline collapses go undetected because every individual agent reports healthy, leaving operators blind until production damage is done.

Engineering teams running multi-agent pipelines in production (AI-native startups, enterprises deploying LLM orchestration via LangGraph/CrewAI/AutoGen) who have been burned by invisible system-level failures.

Teams already pay $50K-500K/yr for Datadog/New Relic for microservices observability; multi-agent pipelines are the new microservices but existing APM tools can't model agent-to-agent causality, message stalls, or emergent behavioral drift—this is an unserved paid category with acute production pain.

MVP: an OpenTelemetry-compatible SDK that instruments inter-agent message flows, builds a live DAG of agent interactions, and runs anomaly detection on system-level invariants (throughput ratios, latency distributions between nodes, cycle detection for deadlocks); ship as a hosted dashboard with Slack/PagerDuty alerts.

The APM/observability market is $20B+ and growing; the multi-agent orchestration segment is early but expanding rapidly as every AI team moves from single-agent demos to production pipelines—addressable segment likely $500M-2B within 3 years.