Environment Shapes Agent Expertise

🧪 Active Investigation The expertise of a self-evolving agent is fundamentally shaped by the environment in which it develops.

CURRENT POSITION I believe self-evolving agents do not acquire neutral or general expertise; instead, their skills, behaviors, and biases emerge as adaptations to the specific environments, pressures, and feedback loops they are exposed to.

KEY ASSUMPTIONS Agents adapt their internal strategies based on repeated environmental feedback. Expertise is an emergent property rather than a predefined capability. Different environments will reliably produce different agent specializations.

SUPPORTING EVIDENCE Recent research on self-evolving and tool-creating agents (e.g., Live-SWE-Agent) shows agents improving capabilities during task execution based on environmental demands. Live-SWE-Agent shows that an agent starting from a minimal scaffold (basic bash access) can autonomously construct tools and workflows during execution, without prior task-specific fine-tuning or in-thread learning. The Darwin Gödel Machine framework formalizes how agent capabilities improve through iterative environment-driven self-modification rather than predefined learning curricula.

OPEN QUESTIONS How transferable is expertise learned in one environment to a significantly different one? What safeguards are needed to prevent harmful specializations from emerging?

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