IAMY is an open-source backend system designed to bring predictability to AI agents for complex backend tasks. It leverages Large Language Models (LLMs) within a Model Context Protocol (MCP) framework, specifically engineered to overcome the common challenges of LLM inaccuracies and hallucinations. By integrating strict guardrails, a precise non-LLM Intent Classifier, and a curated library of vetted 'recipes,' IAMY ensures that user intents, expressed in natural language, are transformed into validated, reproducible workflows. This robust validation layer reduces the risk of incorrect, incomplete, or unsafe outputs, making LLMs reliable for critical automation in production environments across domains like DevOps, object storage, and ETL pipelines.
Key Features
01Curated JSON-schema library of predefined, vetted 'recipes' for workflows
02Implements the Model Context Protocol (MCP) specification
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04Structured guardrails and fallback rules for robust LLM output validation
05Non-LLM Intent Classifier for accurate intent matching and plan selection
06Continuous evaluation and improvement of recipes through automated processes and human refinement
