Prevent production AI agent breakage — automated testing, observability, runtime safety

Production AI agents—multi-step, stateful orchestrations that call LLMs, tools, and internal data—are breaking in ways traditional testing and monitoring miss: incorrect actions, state corruption, emergent loops and silent drift that surface only in production. These failures are encountered by SREs, ML engineers, platform teams and product owners at an estimated 500,000 software organizations now adopting agent patterns, and the cost of outages, remediation and regulatory exposure is rising as agents touch more critical workflows. You could build an integrated platform that combines agent-aware automated testing (scenario-driven unit/integration tests, adversarial/fuzzing for tool use), production observability with causal tracing and state snapshots, and a runtime safety layer offering policy enforcement, circuit breakers and automated mitigation playbooks plus immutable audit logs for compliance. Deliver SDKs and CI/CD integrations for major LLM APIs and orchestration frameworks, with both managed and on-prem options, targeting an attainable ACV in the neighborhood of $36K per organization in the mid/enterprise market. This is an attractive window: LLM commoditization and standardized APIs lower integration friction while the “agentization” trend increases failure surface area, and growing regulatory scrutiny (for example, the EU AI Act) creates demand for auditable monitoring—together supporting an $18B addressable market and a high market score. To stand out you must be explicitly agent-aware (not just LLM observability), minimize false positives with causal analysis, and ship enterprise-grade controls and auditability; the chief challenges are nontrivial engineering to instrument stateful agents, customer integration friction, and paying the sales/CS cost to win mid-market and enterprise deals in a moderately competitive field.