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

AI agents stall by asking too much — add policy-based decision frameworks targets a $24.0B = 1,000,000 organizations (teams building or adopting AI agents) x $24K ACV (tooling, consulting, run-time, templates) total addressable market with medium saturation and a year-over-year growth rate of 30-45% — tooling around LLM ops and agentization is expanding rapidly.

Key trends driving demand: Agentization -- more products are exposing autonomous agents that need runtime decisioning, increasing demand for policy frameworks.; LLM capability leaps -- stronger LLM planning and tool use make autonomous action feasible, amplifying edge cases where policies are required.; Shift to observability & safety -- enterprises demand auditable, testable decision logic for compliance and risk control.; Composable tooling -- emergence of LLM toolchains and hosted runtimes reduces time-to-market for agent frameworks..