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Hey HNSalman, Shuguang and Adil here from Katanemo Labs (a DigitalOcean company).Wanted to introduce our latest research on agentic systems called Signals. If you've been building agents, you've probably noticed that there are far too many agent traces/trajectories to review one by one, and using humans or extra LLM calls to inspect all of them gets expensive really fast. The paper proposes a lightweight way to compute structured “signals” from live agent interactions so you can surface the trajectories most worth looking at, without changing the agent’s online behavior. Computing Signals doesn't require a GPU.Signals are grouped into a simple taxonomy across interaction, execution, and environment patterns, including things like misalignment, stagnation, disengagement, failure, looping, and exhaustion. In an annotation study on τ-bench, signal-based sampling reached an 82% informativeness rate versus 54% for random sampling, which translated to a 1.52x efficiency gain per informative trajectory.Paper: arXiv 2604.00356.
Project where Signals are already implemented: https://github.com/katanemo/planoHappy to answer questions on the taxonomy, implementation details, or where this breaks down.
Show HN: Signals – finding the most informative agent traces without LLM judges
A lightweight, GPU-free method to surface the most informative agent trajectories, offering a 1.52x efficiency gain over random sampling, without relying on expensive human or LLM judges.
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A lightweight, GPU-free method to surface the most informative agent trajectories, offering a 1.52x efficiency gain over random sampling, without relying on expensive human or LLM judges.
Signals addresses a critical scalability and cost challenge in the burgeoning field of AI agent development: the overwhelming volume and expense of evaluating agent performance. By providing a lightweight, non-GPU dependent method to identify 'informative' traces, it significantly reduces the operational cost and human effort associated with debugging and improving agentic systems. The reported 1.52x efficiency gain per informative trajectory is a compelling metric for developers struggling with agent observability. This solution capitalizes on the growing need for robust monitoring and evaluation frameworks for AI agents, particularly as agentic architectures become more prevalent. This project indicates a strong market for tools that optimize the development lifecycle of complex AI systems by making debugging more targeted and cost-effective.
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Based on metadata extraction, Signals – finding the most informative agent traces without LLM judges is categorized under topics such as: agentic systems, agent traces, trajectories, LLM judges.
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