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Tweet discussing advice on self-improving agents, with personal observations from experiments on coding agents for long-horizon tasks, noting that stronger models don't always yield better agents.
HALO uses RLMs to optimize AI agent harnesses by analyzing execution traces and suggesting improvements, achieving 10%+ gains on several benchmarks like Terminal-Bench and AppWorld.
The article discusses new research from Sakana AI and Meta on self-improving AI agents, specifically the Darwin-Gödel Machine and Hyperagents, which autonomously rewrite their own code and infrastructure to enhance performance without human intervention.
Hermes Agent demonstrates self-improvement capabilities by observing its own performance, identifying inefficiencies, and rewriting its skills to achieve a 3x speedup and 80% cost reduction in just two iterations.
A paper introduces a protocol framework for self-improving AI agents, enabling auditable improvement proposals, assessments, and rollbacks.