SOPHIA Meta-Agent for AI Agent Maintenance

SOPHIA is a meta-agent designed to address the practical problem of AI agent degradation in production ecosystems. When running agents for sales, clinical documentation, and customer service, prompts can go stale, tools drift, and user behavior shifts over time. SOPHIA acts as a Chief Learning Officer that observes, diagnoses, researches, and proposes improvements to every other agent in the system.

Design Process and Technical Contributions

The meta-agent was designed through an experimental process using 4 frontier models across 7 iterations:

• Claude → Gemini → ChatGPT → Grok, with each model iterating on the previous version
• Peer review across all three models, triage, and final integration

Key technical contributions by model:

• Gemini: Actor-Critic paradigm (agents as Actors, Sophia as Critic)
• ChatGPT: Anti-Goodhart guardrails, Tool Contract Registry, Reproducibility
• Grok: Evolver (evolutionary prompt search), Agent-as-Judge, Meta-Sophia

Operational Details

The system requires human approval for all changes - no deployment occurs without explicit sign-off. This ensures that while SOPHIA can propose improvements, human oversight maintains control over the production environment.

The full design process and implementation details are documented in the project's repository, which includes the iterative development approach and specific contributions from each model used in the design.