Use case
After connecting AI agents to internal systems, enterprise security teams handle agent ownership, accessible data scope and runtime operation logs to discover, prioritize and remediate each agent's behavior risk.
Enterprises currently rely on generic logging platforms, manual audits or existing endpoint and identity security tools stitched together, lacking dedicated discovery and risk prioritization for agent behavior.
Agents autonomously call tools and access data, which account- and endpoint-oriented monitoring struggles to cover; security teams do not know which agents exist, who owns them or what they can access, making incidents hard to trace.
xOcto's call
Demand is evidenced
The trend is that AI agents are entering enterprise production environments, creating demand to monitor and govern agent behavior itself rather than only protecting traditional accounts and endpoints. The entry point could be security teams in highly regulated sectors such as finance and healthcare, potentially priced per managed agent or per compliance audit deliverable, though no public pricing is disclosed and this is inference.
Reason to use it
Why users would choose it
Inference: compared with generic logs plus manual audit, Reco automatically discovers agents across 280+ apps, labels ownership and access scope and prioritizes risk, cutting the step of reading logs line by line and manually inventorying agents, so security teams in regulated sectors that already deploy agents may adopt it around rollout; public materials provide no usage or retention evidence.
Where the easy answer breaks down
The tension worth following
An English validation note will follow from the public evidence.
If this is your job
Investigate further. Inference: compared with generic logs plus manual audit, Reco automatically discovers agents across 280+ apps, labels ownership and access scope and prioritizes risk, cutting the step of reading logs line by line and manually inventorying agents, so security teams in regulated sectors that already deploy agents may adopt it around rollout; public materials provide no usage or retention evidence.
Entry and what to borrow
The trend is that AI agents are entering enterprise production environments, creating demand to monitor and govern agent behavior itself rather than only protecting traditional accounts and endpoints. The entry point could be security teams in highly regulated sectors such as finance and healthcare, potentially priced per managed agent or per compliance audit deliverable, though no public pricing is disclosed and this is inference.