AI agents stall by asking too much — add policy-based decision frameworks

Teams building autonomous agents — platform engineers, developer teams embedding agentic workflows, and enterprise risk/compliance groups — are increasingly encountering a common failure mode: agents stall because they default to asking clarifying questions or re-checking constraints instead of taking bounded actions, creating latency, poor UX, and auditability gaps. This problem amplifies across organizations that already deploy multiple agent types; using the market proxy of 1,000,000 potential organizations and a $24K average annual contract value, the addressable market is roughly $24.0B and the opportunity for a focused tooling stack is substantial but not trivial to capture. A practical product would be a policy-based decision framework that sits between planners and executors: a declarative policy language plus runtime, SDKs for common agent frameworks, prebuilt enterprise templates (data access rules, cost/latency budgets, escalation paths), test/simulation suites, and immutable audit logs for compliance. Packaged offerings could include hosting/runtime, consulting for integration, and a template marketplace — matching the $24K ACV model — while emphasizing low-latency enforcement and easy rollback so agents don’t degrade user experience. This is an attractive moment because agentization, improved LLM planning/tool use, and rising demands for observability and safety are all converging; the market score (92/100) and revenue potential (88/100) reflect strong tailwinds. Standing out will require honest handling of hard engineering trade-offs — minimizing enforcement latency, proving policy correctness, and integrating across heterogeneous agent stacks — so defensibility comes from enterprise-grade auditability, testable policy semantics, vertical templates, and partnerships with major agent platforms rather than relying on marginal feature parity.