agentic_TRACE is a framework designed to build LLM-powered data analysis agents that prioritize data integrity and auditability. It addresses the risks associated with directly feeding data to LLMs, such as fabrication, inaccurate calculations, and context window limitations. The core principle is to separate the LLM's orchestration role from the actual data processing, which is handled by deterministic tools.
This approach ensures prompts remain concise, minimizes hallucination risks, and provides a complete audit trail of data transformations. The framework is domain-agnostic, allowing users to extend it with custom tools and data sources for specific applications. A working example, focusing on stock market analysis, demonstrates its capabilities.
This approach ensures prompts remain concise, minimizes hallucination risks, and provides a complete audit trail of data transformations. The framework is domain-agnostic, allowing users to extend it with custom tools and data sources for specific applications. A working example, focusing on stock market analysis, demonstrates its capabilities.
Learn how to equip your Microsoft Agent Framework agents with portable, reusable skill packages that provide domain expertise on demand using Agent Skills. This article covers what Agent Skills are, progressive disclosure, creating skills, connecting skills to an agent (with .NET and Python examples), use cases, and security considerations.
Minimalist LLM Framework in 100 Lines. Enable LLMs to Program Themselves.
