Enterprise multi-agent AI systems produce thousands of inter-agent interactions per hour, yet existing observability tools capture these dependencies without enforcing anything. OpenTelemetry and Langfuse collect telemetry but treat governance as a downstream analytics concern, not a real-time enforcement target. The result is an “observe-but-do-not-act” gap where policy violations are detected only after damage is done. We present Governance-Aware Agent Telemetry (GAAT), a reference architecture that closes the loop between telemetry collection and automated policy enforcement for multi-agent… Read More
Governance-Aware Agent Telemetry for Closed-Loop Enforcement in Multi-Agent AI Systems
Enterprise multi-agent AI systems produce thousands of inter-agent interactions per hour, yet existing observability tools capture these dependencies without enforcing anything. OpenTelemetry and Langfuse collect telemetry but treat governance as a downstream analytics concern, not a real-time enforcement target. The result is an “observe-but-do-not-act” gap where policy violations are detected only after damage is done. We present Governance-Aware Agent Telemetry (GAAT), a reference architecture that closes the loop between telemetry collection and automated policy enforcement for multi-agent…
Related Posts
Building Smart Machine Learning in Low-Resource Settings
Most people who want to build
Switchboard-Affect: Emotion Perception Labels from Conversational Speech
Understanding the nuances of speech emotion dataset curation and labeling is essential for assessing speech…
Project Genie: Experimenting with infinite, interactive worlds
Google AI Ultra subscribers in the U.S. can now try out Project Genie.