Manage unreliable LLM agents at scale — runtime ops for autonomous agents

About 300,000 development-centric organizations deploying autonomous LLM agents are encountering a new class of operational problems: nondeterministic failures, hallucinations, cascading retries, and unexpected cost spikes that traditional SRE and observability tooling does not surface at token or prompt granularity. Those teams face delayed rollouts, higher incident MTTR, and stalled adoption because they cannot set SLOs, trace multi-model executions, or enforce runtime policies across cloud-native environments. You could build a runtime ops platform for autonomous agents composed of a lightweight in-environment agent (sidecar/operator) plus a SaaS control plane and developer SDKs that deliver token-level traces, hallucination and provenance detection, policy enforcement, canary/rollback primitives, cost attribution, chaos testing, and human-in-the-loop fallbacks. Integrate with OpenTelemetry, Kubernetes and serverless platforms, major model APIs, and incumbent SRE stacks so teams get familiar trace/metric/log style observability and can adopt incrementally; target enterprise ACV near $100K consistent with the $30B TAM estimate. This market is attractive now because LLM and agent proliferation, combined with cloud-native standardization, create urgent demand and a large addressable market ($30.0B, market score 95/100, revenue potential 82/100). Competition is medium: incumbent observability vendors and a few AI-focused startups exist, but you can differentiate by offering measurable reliability guarantees for nondeterministic workloads, deep runtime controls that map to SRE workflows, and pragmatic integrations that minimize lift for platform teams; honest challenges remain around engineering complexity, model API fragmentation, data security requirements, and longer enterprise sales cycles.