Prevent runaway AI agents with runtime guardrails and observability

Executive Summary

Enterprises and platform and security teams at roughly 1.5M companies with 50+ employees are rapidly adopting autonomous agents for internal automation and developer tooling, creating tangible risks from runaway agents that can exfiltrate data, execute unintended actions, or chain together into unsafe workflows. Existing controls tend to focus on static checks, API logging, or model-level monitoring and often miss cross-tool, runtime behaviors where most of the risk actually manifests. You could build a runtime guardrail and observability platform that enforces policy-as-code at execution time, provides end-to-end provenance across models and tools, performs action validation (allow/deny/human-in-loop), and streams structured telemetry into SIEM/SOAR and CI/CD pipelines. Core capabilities would include low-latency interception, model-agnostic instrumentation, behavioral anomaly detection with explainable alerts, and automated containment or rollback, delivered as an agent-side SDK plus a managed control plane. This is timely: the $30.0B addressable market (1.5M companies × ~$20K annual spend on application/cloud security) combined with accelerating agent adoption, model-supply-chain complexity, and regulatory pressure means buyers are actively looking for runtime governance beyond shift-left checks. To stand out you must demonstrate materially better runtime coverage and lower false positives than incumbents, offer vendor-agnostic observability with turnkey CI/CD and cloud integrations, and align pricing to the ~ $20K budget line item that many buyers already allocate for application security. The challenges are real—integrating with diverse agent frameworks and models, overcoming the incumbent competition, and proving ROI in a conservative security buying cycle—but if you can deliver reliable, explainable enforcement that measurably reduces incident cost, the commercial opportunity is substantial.