Building an Autonomous AI Judge Agent: A Complete Implementation Integrating RAG Retrieval, Tool Calling, and Structured Output

This article introduces the open-source project "Interactive Judge Agent", which aims to solve the problem of hallucinations in traditional large language models caused by being frozen in training data and unable to access real-time/private data. The project integrates technologies such as Ollama local inference, RAG retrieval, tool calling, and Pydantic structured output to implement a complete workflow for autonomous decision-making and risk assessment. It demonstrates the application value of Agentic AI using the AI Construction Judge scenario as an example.

Traditional AI demos often stop at simple text generation and cannot meet the needs of real enterprise scenarios: the model needs to understand user intent, determine when external information is needed, autonomously call tools to obtain data, reason based on facts, and output formatted results. The project uses the "AI Construction Judge" as a scenario to simulate an autonomous analyst evaluating construction and financial risks. The core challenge is to upgrade the model from "answering questions" to "completing tasks autonomously".

The system adopts a layered architecture: the tool layer is responsible for RAG retrieval (e.g., calling the search_insights function to query the database); the schema layer uses Pydantic to define structured outputs (e.g., the JudgeVerdict schema includes status, severity, risk score, etc.); the agent layer is the core, coordinating the entire process through the process_user_query function (receiving queries → analyzing → calling tools → integrating results → generating verdicts); the validation layer ensures outputs conform to Pydantic structures. The tech stack prioritizes local deployment: Ollama runs open-source models (e.g., Qwen2.5), OpenAI-compatible SDKs, Sentence Transformers for semantic embedding; the tool calling mechanism allows the model to actively judge and call tools, and structured output validation prevents hallucinations.

Complete workflow: User inputs query → LLM understands intent → determines if external information is needed → calls tools for retrieval → reasons based on evidence → Pydantic validation → outputs structured results. A typical example: When the user asks "Is steel transportation normal?", the system calls a search tool and finds "Steel transportation is delayed by two weeks, affecting Q3 delivery". Finally, it generates a structured verdict: status is FAIL, severity is HIGH, risk score is 9/10, with a detailed explanation attached.

This project is a production-grade AI engineering example, demonstrating practices such as clean layered architecture, scalable tool systems, and reliable output validation; it provides developers with transferable implementation patterns (tool definition and registration, structured output design, agent loop orchestration, etc.) that can be applied to scenarios like customer service automation and data analysis assistants; it proves that open-source toolchains (Ollama + open-source models + Python) can support complex AI applications, promoting the democratization and local deployment of AI technology.

The project currently uses a simulated database. Future plans include upgrades: integrating a real FAISS vector database for semantic search, adding persistent memory functionality, supporting multi-tool calling and multi-agent collaboration, implementing streaming responses, building a web dashboard interface, and integrating with LangGraph and LangChain frameworks. These directions reflect the evolution path of enterprise-level AI agents from single tool calling to complex multi-step reasoning, and from discrete interactions to continuous dialogue memory.