AgenticAI RFP - Integrated Long Term Memory MCP Server - Extract Past Proposals from Knowledge Base
What is the difference between a smart tool and a wise expert? Experience.
In our previous episodes, our RFP Analyzer Agent learned to understand documents, perform cost estimations, and even search the web. But it had a critical flaw: it suffered from digital amnesia. Every task was its first, and every solution was forgotten the moment it was delivered.
In Part 5 of our Agentic AI series, we solve this for good. We are giving our agent a Long-Term Memory, transforming it from a single-use tool into a learning expert that builds a permanent, queryable knowledge base from every proposal it creates. We'll show you how this allows the agent to reference past successes, ensure consistency for repeat clients, and dramatically accelerate the creation of new proposals by reusing proven architectural patterns.
This isn't just about storing data; it's about creating an AI with institutional memory.
In this video, we'll cover:
✅ The "Why": How a persistent memory solves the critical business problem of losing valuable institutional knowledge.
✅ Architecture & Code: A line-by-line walkthrough of the memory_server.py (using FAISS and Sentence-Transformers) and the logic updates in rfp_analyzer_agent.py to create a "recall-then-memorize" loop.
✅ End-to-End Flow Explained: We'll trace the request from the Gradio UI, through the agent's thinking process, to the Memory Server, and back, showing exactly how the knowledge base is used.
✅ Live Demo - The Knowledge Base in Action: We will ask our agent a series of intelligent, semantic questions about past proposals to demonstrate how it can instantly provide strategic insights.
✅ The Future of Agentic Memory: A discussion on future enhancements to make this system even more autonomous, reducing the need for manual review.
Join us as we take a critical step towards building a truly autonomous AI Pre-Sales Team Lead!
https://www.youtube.com/watch?v=IWlIs2As7vs
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