How We Built an Agent That Improves Itself — Zubin Aysola, Weights & Biases
Summary
The presentation details the architecture of ARIA, a self-improving AI agent developed by Weights & Biases. The core innovation is a robust, closed-loop evaluation framework that allows the agent to perform 'auto-research' by taking production traces, converting them into offline evaluation tasks, identifying bugs (e.g., missing SDK calls), writing fixes, and benchmarking the improved variant against the production version. This system ensures that both production and research environments are byte-for-byte identical, creating a continuous 'eval flywheel' for agent improvement.
Key takeaways
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The Covariance Challenge in Agent Evaluation
2:11
A major challenge is that benchmarks, evaluations, and agent configurations are all covariant. To maintain airtight measurement, the team keeps the production and research agents byte-for-byte identical (5:50).
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The Eval Flywheel
The system creates a continuous improvement loop: every production miss or win is captured as a new task, which is then used to train and improve the agent, minimizing manual benchmark creation (12:03).
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Offline Evaluation Framework
The framework uses a dedicated offline evaluation sandbox to simulate environments and run agent variants. This process involves converting production traces into structured evaluation tasks (3:40).
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Dual Scoring Mechanism
16:54
ARIA scores itself in two ways: pass/fail (normative) and relative comparison, allowing comparison between different agent variants (10:14).
Technical details
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Agent Architecture & Framework
715s
The system uses a model-agnostic harness defined by YAML variants to run multiple parallel agent configurations. This allows for testing multiple mutations of the exact same configuration (7:15).
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Production/Research Sync
590s
The research and production codebases are kept identical, utilizing a 4-hour sync job to prevent drift when new skills or variants are developed (5:50).
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Trace Processing
Production traces logged in Weights & Biases Weave are ripped into the offline evaluation framework, enabling the agent to 'hill climb' on real-world errors (3:40).
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Sandbox Environment
804s
The system requires an unconstrained sandbox environment to allow the agent to perform parallel executions of its own research loop and discover emergent behaviors (8:04).
Mentioned resources
- Weights & Biases (W&B)
- W&B Weave
- ARIA Agent
Channel & topics
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