Worked across HKUDS/LightRAG, pydantic-ai, and agno-agi/agno to deliver three new features focused on AI workflow reliability and traceability. Enhanced LightRAG by enabling offline evaluation of retrieval samples and refining audit logic to exclude zero-scoring documents, improving validation accuracy without requiring live API calls. In pydantic-ai, preserved tool result identifiers to support better tracking of explainable AI tool responses. For agno-agi/agno, introduced default knowledge filter attributes and regression tests to strengthen knowledge management and prevent delegation errors. Leveraged Python, API integration, and unit testing to improve evaluation, auditing, and knowledge filtering across these core backend components.