Five roles for an Ollama-based workflow: architecture walkthrough + Python scaffold

Hi everyone — I'm behind Autonomous Workday, and I'm sharing an English-language architecture walkthrough about building an event-driven workflow around local inference. The proposed flow separates five roles: Sentry: ingest and triage incoming work. Synthesizer: retrieve relevant context. Builder: execute the task. Critic: review the result and send it back for revision when needed. Dispatcher: hand the result to a human for approval. The episode discusses Ollama as part of the local stack, plus optional cloud escalation. The design question is how to pass structured data between these roles and place approval boundaries. I also published companion Python examples with Pydantic schemas, mock triage, and a ChromaDB memory scaffold. The model response is currently mocked; the complete five-role orchestration is still a blueprint. Episode 01 (~9 minutes): https://www.youtube.com/watch?v=f9unpl5Fh9g Companion examples: https://github.com/jo481-cell/autonomous-systems-templates On your local setup, which part would you prototype first: inbox triage, context retrieval, or a separate review step? Disclosure: this is my channel, and this post was drafted with AI assistance. submitted by /u/KernelNomad_404 [link] [comments]

Built a local 3-agent coding system (Architect/Executor/Reviewer) with qwen3-coder:30b + Ollama + OpenCode – here's what actually works and what doesn't

Source: r/ollama | 2026-10-01

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