Tag
Oodle launches Agent Observability on Hacker News, offering agent traces at $10 per million spans to help AI-native teams detect silent failures and improve reliability.
The tweet discusses the concept of self-learning as a competitive moat, highlighting browser activity and agent traces as key data sources, and introduces AG-UI, an open standard for capturing user-agent interactions to improve products.
The article discusses a method for building self-learning agents by combining agent traces with in-browser user activity, using the AG-UI protocol to capture and apply learnings, enabling products to improve through usage and create a business moat.
Arize Phoenix announces addition of trace search functionality to handle increasingly long and complex agent traces, allowing users to search across traces and view call stacks for spans.
A paper proposing a mechanism for enterprise agents to improve by safely converting messy daily work into learning data, using a data proxy and control layer, with AREAL2.0 demonstrating online RL from real interaction traces.
Morph Reflexes offers fast, cheap semantic signals from agent traces using multi-head classifiers on a shared backbone, enabling scalable monitoring of production agents.
Skill-DisCo is a framework that distills reusable procedural skills from successful agent traces and compiles them into callable, executable programs. Experiments on ALFWorld and WebArena show improved success rates and reduced agent turns.
This paper empirically analyzes the cost-effectiveness of code execution in LLM-based program repair agents, finding that execution is used heavily but often indiscriminately, and that restricting execution can save significant cost with minimal impact on repair success.
SelfCompact is a scaffolding approach that lets language models autonomously decide when and how to compact long agent traces, achieving better performance with reduced token costs compared to fixed-interval methods.
A quick `hf sync` command helps sync agent session traces so they are never lost and can be used across machines.
Harrison Chase announces a post-trained model for detecting issues in production agent traces, claiming SOTA accuracy at 10-100x cheaper rates than frontier models.
LangChain engineers detail how they built a custom inverted index for SmithDB to support full-text search and JSON filtering over large agent traces stored in object storage, achieving median latency of 400ms despite huge payload sizes.
Introduces RedAct, a framework to protect agent traces from procedural skill leakage by selectively redacting sensitive details while preserving audit evidence, along with CapTraceBench for evaluation.
TRL now supports fine-tuning models on agent traces from various sources like Claude Code, Codex, OpenClaw, and Pi, moving towards a standardized stack for training agentic models.
Introduces Reasoning Primitive Induction, a method that mines successful ReAct traces to cluster recurrent reasoning moves into typed pseudo-tools, outperforming the original agent by tens of percentage points on benchmarks.
New documentation page for rendering Agent Traces on the Hugging Face Hub has been released.
A library called teich converts agent traces into supervised fine-tuning (SFT) datasets, simplifying dataset preparation for AI training.
ZenithDB is a new open-source Rust database designed for storing and querying AI agent traces. It achieves sub-millisecond trace fetch latency at 1 billion rows by colocating all spans of a trace during compaction, and includes embedded full-text search and late materialization.