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

**[AI Engineer](https://daily.dev/sources/aidotengineer)** · 20 min read · 0 upvotes · 0 comments

## Summary

Jason Lopatecki, founder of Arize, describes a vision for self-improving AI systems where observability data (traces, logs, evals) feeds autonomous agents that detect issues and generate fixes or PRs with minimal human intervention. He introduces Arize's 'Signal' product, which runs periodically in sandboxes, uses composable skills to pull context from observability platforms, and surfaces issues with evidence before a human even looks at them. The talk covers the shift from human-driven debugging to agent-assisted workflows, the importance of well-designed skills for data retrieval, the role of online evals as preprocessed signals layered onto production traces, and the architectural pattern of moving local debugging workflows into cloud sandboxes triggered by events or schedules.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=9HbzAWnKbo4>

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---

Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#observability](https://daily.dev/tags/observability)

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