Prevent autonomous agents from acting until a rigorous manual-run phase is complete

Enterprises that are deploying autonomous agents for customer service, remediation, or decision-making are increasingly exposed to security, compliance, and business-risk incidents when those agents take unsupervised actions; this problem is acute in regulated sectors (finance, healthcare, telecommunications) and affects the security/compliance, risk, and platform teams at an estimated 500,000 enterprises. The core pain is lack of repeatable, auditable human-run gates before agents perform high-risk operations, leading to incidents, failed audits, and regulatory exposure. You could build a pre-execution control platform that prevents autonomous agents from acting until a configurable, rigorous manual-run phase is complete: a policy engine for fine-grained gating, real-time approval workflows with low-latency UX, sandboxed replay and evidence capture for auditors, and cryptographic attestations that bind approvals to agent executions. The product would integrate with popular agent orchestration frameworks and existing MLOps/ITSM tooling to minimize lift for engineering teams while producing the evidence regulators and internal auditors require. This is an attractive moment: we estimate a $12.0B addressable market (500,000 enterprises × $24K ACV), and the demand is amplified by trends such as agentification of workflows, the EU AI Act and similar regulatory pressures, and a broader shift toward human-in-the-loop MLOps (Market Score 92/100, Revenue Potential 90/100). To stand out against a medium-competition field you should prioritize developer ergonomics and low-friction approvals, provide enterprise-grade security certifications and verifiable audit trails, and go deep in one regulated vertical as a beachhead; the honest challenges are real—customers will resist added latency and complexity, integrations will be nontrivial, and convincing organizations to change runbooks requires strong proof points and partnerships.