Prevent runaway AI agents with layered governance and persistent memory

Enterprises building multi-step, autonomous AI agents increasingly confront "runaway" behaviors: agents executing unintended actions, leaking sensitive data into persistent memories, or making irreversible decisions without human oversight. This risk is concentrated in 50,000 mid-to-large enterprises in regulated industries (finance, healthcare, energy) that demand traceability, human overrides and provable audit trails for automated workflows. You could build a developer platform that combines layered governance with encrypted, versioned persistent memory: a runtime policy engine enforcing hard and soft constraints, fine-grained RBAC and SSO, human-in-the-loop kill-switches, deterministic audit logs and SDKs that integrate with LangChain, Hugging Face and OpenAI. The product would offer deploy options (SaaS, VPC) and measurable SLAs for safety, plus explainability tooling for regulators and internal auditors. The timing favors this play: agentization of LLMs and composable AI stacks are driving demand for orchestration, and regulated buyers are willing to pay premium contracts—our $30.0B market estimate assumes 50,000 enterprises at an average $600K ACV; Market Score 90/100 and Revenue Potential 92/100 reflect strong willingness to invest. To stand out, focus on the unique coupling of persistent-memory controls with policy-as-code and interoperable developer APIs, validated by third-party security certifications and case studies showing reduced incident rates. Be honest about challenges: integration complexity, latency and cost trade-offs, medium competition from orchestration frameworks and cloud vendors, and the need to prove concrete ROI and compliance outcomes before large enterprises will adopt.