Runtime-security and audit layer that gates every action an autonomous AI agent takes before execution
A purpose-built authorization, sandboxing, and action-audit runtime for production AI agents that intercepts and validates every tool call, payment, API invocation, and state mutation before it fires, with per-agent programmable budgets, kill-switches, and cryptographic proof of execution for compliance and incident investigation.
The problem
AI agents deployed into SaaS platforms and enterprise workflows are shipping without security controls. Teams build agents that can read files, call APIs, move money, and mutate databases, but have no way to scope what an agent can do, audit what it did, catch compromised or jailbroken agents in real time, or prove to auditors that every action was authorized. When an agent gets corrupted or an LLM output injection occurs, the blast radius is unlimited and the forensic trail is nonexistent.
Who has it: Series-B and Series-C SaaS companies deploying multi-tenant AI agents (e.g., AI-powered helpdesk, workflow automation, data processing platforms) where agents can call external APIs, access customer data, or trigger payment/refund operations on behalf of end users.
Why now: AI agents are moving from prototype to production across SaaS companies and enterprises (2024–2025). Regulatory bodies and security teams are beginning to ask for audit logs and proof of authorization for agent actions. The first major agent-security incident will force the market. No incumbent (Okta, HashiCorp Vault, traditional APM/observability) has built an agent-native control plane.
Where this came from
2 public sources behind this idea.
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