Agent execution frameworks provide no native infrastructure for detecting silent failures — tasks that appear completed but are incorrect, incomplete, or broken at a dependency level. The cost of delegation is therefore not execution but invisible verification work that users must build themselves, creating a scaling bottleneck as agent deployments grow. A platform-level verification and validation layer would be more efficient than requiring each deployer to instrument monitoring independently.

Agent frameworks silently fail — tasks look complete but are wrong, incomplete, or break downstream dependencies, forcing every deployer to build custom verification from scratch.

Engineering teams running multi-agent systems in production (DevOps, ML platform teams, AI-native startups with 5+ agents in workflows).

Observability for traditional software is a $20B+ market because silent failures are existential at scale; agent deployments are hitting the same wall NOW but with zero tooling, and teams are already paying engineers to hand-build verification scripts.

SDK that wraps popular agent frameworks (LangChain, CrewAI, AutoGen) as middleware — intercepts task outputs, runs configurable assertion checks (schema validation, semantic consistency, dependency state checks via lightweight verifier agents), and emits structured alerts; MVP is a Python package plus a hosted dashboard.

Agent observability is a new wedge into the $25B+ APM/observability market; even 1% penetration of teams deploying agents in production within 2 years is $250M+.