Adaline has launched a new self-improvement layer designed for AI agents that transforms messy production traces into fresh evaluations, synthetic edge cases, and improved candidates for human approval. This innovative tool analyzes production traffic and user feedback to organize agent behaviors without manual inspection, facilitating a more streamlined approach to identifying and resolving issues. This launch aligns with recent industry discussions on agent metabolism as a continuous improvement architecture, highlighting the importance of processing production traffic to automatically validate enhancements.

Adaline: Adaline is an observability and evals platform for self-improving AI agents that converts production traces into behaviors, issues, auto-generated evaluations, and synthetic data. It recently launched Adaline 2.0 as the agent self-improvement layer to automate the creation of fresh evals and candidate agents from real traffic and feedback. The platform enables teams to review and ship improved agent versions without manual inspection of every interaction.
Arshdil Bagi: Arshdil Bagi, also referred to as Arsh Shah Dilbagi, is the Co-Founder and Chief Executive Officer of Adaline. He introduced Adaline 2.0 on social media, outlining how the system turns traces into behaviors, surfaces issues, and generates evaluations plus new agent candidates for human review. Bagi drives the company’s focus on infrastructure that supports autonomous improvement of production AI agents.

Agent Infrastructure: Recent industry discussions describe agent metabolism as a continuous improvement architecture that processes production traffic to discover behavioral patterns and validate enhancements automatically.
Platform Capabilities: Adaline integrates observability with evaluation generation to help teams move from raw traces directly to testable agent improvements in a unified workflow.