Most deployed agents run with full access to filesystems, networks, credentials, and shell—because no standard tiered permission model or approval workflow primitive exists in agent frameworks. Developers implement ad-hoc safety checks inconsistently, and a single bad tool call can delete data or leak secrets with no circuit breaker. A platform-level allowlist and staged approval layer for destructive or external operations would benefit every agent deployment but does not exist as shared infrastructure.

Agents today run with god-mode access because no standard permission/approval primitive exists, meaning a single bad tool call can delete data, leak secrets, or incur costs with zero circuit breaker.

Engineering teams deploying LLM agents in production (DevOps, platform engineers, AI eng leads) at companies from seed-stage to enterprise who need to ship agents without existential risk.

Every team deploying agents reinvents ad-hoc safety checks; this is the IAM layer for the agent era — companies already pay for human IAM (Okta $18B), and agent permissions are more urgent because failures are automated and instant.

MVP is an open-source middleware SDK (Python/TS) that wraps tool-call execution with a policy engine (YAML allowlists per action tier), async human-in-the-loop approval via Slack/email for destructive ops, and an audit log — deploy as a sidecar or decorator around any agent framework (LangChain, CrewAI, OpenAI Agents SDK).

Every company running AI agents in production needs this; adjacent IAM/policy market is $20B+ and agent deployments are growing 10x YoY, making this a multi-billion dollar infrastructure category.