Building the science of runtime AI safety.
LatentOps Research studies how autonomous AI systems behave, fail, and recover when they act in real environments.
Our focus is the action layer: tools, permissions, memory, workflows, external systems, policies, approvals, incidents, and evidence that can be checked before side effects happen.
Research agenda
Runtime safety for agentic systems
Review AI actions before execution: tool calls, data access, workflow changes, file edits, external messages, system updates, and other high-impact actions.
Tool, memory, and permission safety
Study how tools, private context, memory, connectors, permissions, and sensitive resources change the safety boundary for autonomous systems.
Multi-agent safety and control
Understand miscoordination, unsafe delegation, cascading failures, hidden dependencies, accountability gaps, and emergent behavior across networks of agents.
Interpretability for runtime risk
Combine internal signals, behavioral traces, action histories, policy matches, and evidence packets into better runtime risk estimates.
Agent identity and provenance
Track who created an agent, what it can do, what tools it used, what actions it took, and who approved those actions.
Safety cases and AI assurance
Generate deployment evidence from evaluations, traces, policies, incidents, human reviews, runtime controls, and audit trails.
What we publish
Research at LatentOps is designed to become infrastructure: better evaluations, stronger policies, safer tool use, clearer audit trails, and more reliable control over autonomous systems in production.
Build runtime safety with us.
We are building the safety foundation for the agentic internet. If you are working on deployed agent systems, evaluations, security, or AI assurance, we would like to talk.