Sunday Coffee & Code: Successful End-to-End RFP Generation Run (Web UI + Multi-Agent Orchestration)

This week I got a clean end-to-end run of my RFP Factory: upload through the Web UI, multi-agent orchestration does the heavy lifting, and a complete draft package comes out the other side. It’s one thing to have individual components working; it feels like a whole other step to have the whole pipeline behave like a real system. What worked (and why it matters) ● Multi-Agent Orchestration (Microsoft Agent Framework - MAF) MAF performed exceptionally well as the orchestration layer - both for offline Model (Ollama) and OpenAI integration with tool execution. The “agent team” approach is now predictable enough that it feels like engineering, not experimentation. ● Local models via Ollama (Qwen3 continues to impress) Even though this run used OpenAI’s API end-to-end, it reinforced that local models are not just “nice demos.” The Qwen3 family in particular performed strongly for this workflow. ● MCP (Docling via MCP: surprisingly frictionless) Docling via MCP has been one of the simplest pieces to integrate: download it, start it, expose it as a tool to agents, and it just works. That’s exactly what “tooling” should feel like - boring, reliable, repeatable. ● Agentic coding (Anthropic Claude Code from VSCode + Agent) This was a major step-change. Not just “faster coding”, it changes the way I worked. I spent more time on the specification and acceptance criteria up front. Troubleshooting does shift a bit: being more “distant from the code” can mean it takes longer to reacquire context when something breaks. Experience matters here: knowing where to look (configs, log files) helps. What I’ve learned building this (beyond the RFP use case). This project has been a practical vehicle for exploring a stack of emerging patterns and technologies - and it’s clarified a few things for me: ● Agents have matured dramatically since my first builds in 2024. Reliability, tool use, and orchestration patterns are materially better now. ● Specs are the new leverage. The better the spec, the more “agentic coding” compounds across quality and speed. Not entirely sure what is next yet - this has been a bit of a wander, but in the best way: exploring new tech with a practical application in mind. Likely next steps: ● Improve RAG setup (talked about this a few times). ● Automate startup and deployment. ● Add agents to further improve the workflow. ● Build this out as a solution? Cost snapshot (because this part matters) - today’s run: ● 407,743 tokens for $1.45. ● 16.5 minutes of Amazon Web Services (AWS) server time. ● Total of $1.70 for a draft. If you’re experimenting with agents, MCP, or multi-agent orchestration, I’m happy to compare notes - this space is moving quickly, and the practical details are where the learning is. (Lots of screen shots below - front-end, log file, agent activity logs, output zip file contents attached.)