Agents lack enforceable operating rules — policy-as-code for AI agents

Enterprises building multi-step LLM agents today lack enforceable operating rules at runtime, which creates real risks: data leakage, regulatory non-compliance, inconsistent decisioning and auditability gaps that platform, security and legal teams must remediate. This gap affects large organizations broadly—our addressable cohort is roughly 200,000 enterprises moving toward agentization—and current approaches are often manual guardrails or post-hoc detection that cannot provably prevent bad actions. You could build a policy-as-code control plane for AI agents: a declarative, versioned policy language with static analysis, runtime enforcement hooks, simulator/sandboxing, SDKs for major agent frameworks and immutable audit trails that generate both enforcement actions and explainability artifacts. Commercialization would target enterprise ACVs in the $80K–$250K range (our TAM model cites $32.0B = 200,000 enterprises × $160K ACV), with upsell paths for integrations and professional services, while technical work must focus on low-latency enforcement and robust mapping from high-level rules to emergent agent behaviors. This market is attractive now because three trends converge—agentization, the migration of declarative policy-as-code from infra to AI control planes, and intensifying regulatory scrutiny—creating demand and a standards vector; competition today is medium, so early, reliable entrants can build moat. To stand out you must offer provable runtime enforcement (not just detection), an ergonomic policy language, and an open integration ecosystem; be honest that success will require 12–24 months to prove enterprise-grade stability, significant integration effort, and strategic partnerships with cloud and LLM providers.