AX

From Signal to PR: Anatomy of a Self-Improving Agent — Jason Lopatecki, Arize

Jul 24, 2026 · 20:36

Jason Lopatecki demonstrates how Arize's Signal agent transforms observability from human-clicked dashboards into telemetry for self-fixing systems. The key unlock is pulling production traces and logs as files into a repo—Claude Code works magically with files, not dashboards—so the agent sees the exact code path software took. Signal runs periodically or event-based in a sandbox (VPC-deployed for Uber, Booking), using composable skills to gather context, find root cause, and create a PR. Lopatecki argues you should trace and log ten times more because agents can read that smoke. For the question 'why not just point Claude Code at data?', he explains skills must be well-designed to fetch and format the right data into files. On evals, they run as LLM judges layered on production traces, pre-processing information for the agent to catch known failures and create new evaluators.

Recursive Coding Agents - Raymond Weitekamp, OpenProse

Jun 25, 2026 · 23:48

Recursive Language Models (RLMs) are a new test-time compute paradigm that unify reasoning and tool calling, and Raymond Weitekamp of OpenProse argues they can turn coding agents into reliable, recursive problem solvers. He demonstrates that RLMs can process millions of tokens beyond context windows, and a small Qwen 3.5 9B model using RLMs beats GPT-5.4 and Opus on the LongCOT benchmark. Weitekamp shows how Claude Code's Dynamic Workflows now make it an RLM, and how OpenProse allows any coding agent to be recursively orchestrated with declared sub-agent work and dependencies. Use cases include repo-scale migrations, deep research over file systems, and adversarial red-teaming audits. He concludes that RLMs represent the next paradigm of inference-time compute, turning mismanaged genius agents into trustworthy outcomes by capturing golden sessions as reusable workflows.