AI agent sprawl: identify, govern, and consolidate autonomous agents

Enterprises are rapidly deploying autonomous agents across sales, support, engineering and operations, creating fragmented runtimes with little centralized visibility; security, compliance and risk teams at mid-to-large companies face data exfiltration, policy drift and audit exposure as agents proliferate. This is a cross-functional problem — affecting CISOs, compliance officers, and cloud platform teams — and it becomes acute once hundreds of agents and dozens of departmental deployments exist. You could build a cloud-native SaaS that automatically discovers and inventories autonomous agents, ingests model and runtime telemetry via API and lightweight connectors, applies policy-based governance and anomaly detection, and produces consolidation recommendations and automated remediation workflows. Delivered with prebuilt compliance templates and a dashboard quantifying cost and risk reduction, the product targets a $50K ACV per mid/large enterprise buyer profile. The timing is favorable: an $18.0B addressable market (360,000 enterprises × $50K ACV) with a Market Score of 95/100 and Revenue Potential 90/100 aligns with three tailwinds — rapid autonomous-agent adoption, maturation of model observability signals, and cloud-native, API-first infra that makes centralized ingestion feasible without heavy on-prem instrumentation. Buyers are motivated by regulatory pressure and the potential to materially reduce operational overhead and security exposure. To stand out you must be agent-centric rather than model-centric, provide turnkey integrations and measurable consolidation ROI, and ship compliance playbooks for major verticals; strengths include a clear TAM, high willingness-to-pay, and a cloud-native architecture that lowers onboarding friction. Significant challenges remain: integrating diverse agent runtimes, proving concrete ROI in pilots, and competing against established observability and GRC vendors in a medium-competition landscape.