AI-agent failures = workflow failures — governance + orchestration layer

Enterprises building production AI agents and multi-step workflows—roughly 250,000 companies with 50+ employees—are discovering that agent failures are fundamentally workflow failures: orchestration bugs, brittle prompts, downstream integration errors and missing audit trails turn model errors into business and compliance incidents. These problems are felt by platform, engineering and risk teams who face high mean-time-to-detect and remediate, repeated incidents that erode trust, and tangible legal or financial exposure when outputs are wrong or unexplainable. You could build a governance and orchestration layer that combines an SDK-first developer experience with a managed runtime: runtime tracing and lineage, deterministic workflow testing and simulation, fine-grained policy enforcement and explainable audit logs, automatic fallback/retry strategies, and integrations to IAM, SIEM and data catalogs. Packaged as a SaaS plus on-prem connectors with a $120K enterprise ACV motion, the product would emphasize reproducibility (versioned workflows and deterministic replay), developer ergonomics, and out-of-the-box controls for compliance teams. This market is attractive now because agent proliferation increases the number of production workflows that can break, regulatory scrutiny is rising and enterprises are shifting to platformized, SDK-driven adoption—together creating a clear window to capture share in a $30B TAM. The opportunity is real but not trivial: competitors exist and integration complexity, evolving regulations and long enterprise sales cycles are meaningful hurdles, so the defensibility will come from superior runtime observability, provable policy enforcement, and partnerships that embed the product into developer platforms and cloud ecosystems.