Post by Shankarganesh Kumaraganesan

🚀 Excited to share Momentum — my agentic AI architecture project! Momentum started as an evolution of the S10Share reference agent and grew into a modular, observable, tool-enabled AI system capable of reasoning, retrieval, validation, reflection, and replanning. Instead of relying on a single LLM call, Momentum processes requests through a structured pipeline: Query Enrichment → Perception → Context Retrieval → Decision → Planning → Tool Selection → Action → Observation → Validation If validation fails, the system follows a bounded recovery loop: Reflection → Replanning → Action 🔑 Key features: ✅ Asynchronous state-machine architecture ✅ Semantic session memory using FAISS ✅ Local document RAG for PDF, Office, Markdown, JSON, CSV, and text files ✅ Automatic index rebuilding when source data changes ✅ 22 connected MCP tools ✅ DuckDuckGo search and webpage extraction ✅ Persistent and reusable web knowledge ✅ Deterministic timezone-aware current-time tool ✅ Gemini, OpenAI, Anthropic, and local-model support ✅ Independent chat and embedding providers ✅ Structured JSON validation and repair ✅ MCP timeouts and background document indexing ✅ Detailed state-transition and tool-call logging ✅ FastAPI-based Markdown chat interface ✅ 34 passing automated tests One important lesson from this project was that agentic architecture is not simply about adding more LLM calls. It is about defining clear responsibilities, validating every boundary, selecting deterministic tools when appropriate, managing changing information safely, and making the complete execution process observable. I also compared Momentum with the original S10Share architecture, covering the evolution from a manually controlled Perception–Decision–Action loop into a typed and testable state-machine system. 🎥 Watch the project walkthrough: https://lnkd.in/eef3Th57 💻 Explore the source code: https://lnkd.in/eGwW-P-E I’d love to hear your feedback on the architecture, MCP integration, semantic memory, and RAG design. #AgenticAI #ArtificialIntelligence #GenerativeAI #LLM #MCP #ModelContextProtocol #RAG #FAISS #Python #FastAPI #MachineLearning #AIEngineering #SoftwareArchitecture #OpenSource #Gemini #Ollama #LangChain #AIProjects