Environment Shapes Agent Expertise

Question / Claim

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

Key Assumptions

• Agents adapt their internal strategies based on repeated environmental feedback.(high confidence)
• Expertise is an emergent property rather than a predefined capability.(medium confidence)
• Different environments will reliably produce different agent specializations.(high confidence)
• Self-evolving agents can be deployed without task-specific learning occurring initially within the interaction thread; meaningful specialization emerges from environmental interaction over time rather than explicit upfront instruction.(high confidence)
• What appears as 'learning' in self-evolving agents is often the accumulation of environment-shaped tools, heuristics, and decision policies rather than gradient-based learning during deployment.(high confidence)

Evidence & Observations

• 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.(citation)
• 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.(citation)
• The Darwin Gödel Machine framework formalizes how agent capabilities improve through iterative environment-driven self-modification rather than predefined learning curricula.(citation)
• Classical reinforcement learning theory demonstrates that behavior specialization emerges from reward signals and environmental structure, not from explicit instruction of skills.(citation)

Open Uncertainties

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

Current Position