AI_dev Europe 2024

As autonomous agents powered by LLM are deployed in the real world, there is a need for real-time monitoring to detect and mitigate their unpredictable failures. These challenges, including indirect prompt injection and strategic deception, diverge from traditional software issues due to the agents' emergent capabilities and continuous learning. The question arises: how do we ensure our monitoring systems can preemptively address unforeseen failures? This presentation advocates for rigorous evaluations of agent monitoring systems, highlighting the importance of diverse anomaly detection, engaging with more than just chat interfaces, and tackling nuanced issues like ethical boundaries. We propose a community-driven approach to refine LLM agent supervision, featuring a shared database of failure cases and a unified trace format across applications to foster collaborative innovation. Our framework introduce two metrics: i) accuracy on held-out anomaly, simulating the unforeseen failure modes that will emerge on the future, ii) its proficiency in spotting early warning signs before an harmful action. Join us in shaping the future of agent supervision, to anticipate the unexpected!