[{"data":1,"prerenderedAt":129},["ShallowReactive",2],{"blog-post-en-cityos-40000-bildirim-vakasi":3,"blog-siblings-en-cityos-40000-bildirim-vakasi":4},null,{"prev":3,"next":5},{"id":6,"title":7,"author":8,"body":9,"category":112,"cover":3,"date":113,"description":114,"draft":115,"extension":116,"meta":117,"navigation":118,"path":119,"readingTime":120,"seo":121,"stem":122,"tags":123,"__hash__":128},"blog_en\u002Fen\u002Fblog\u002Fflowbit-mcp-ile-otomatik-gorev-yonetimi.md","FlowBit and MCP: automated task management that works","Codifya",{"type":10,"value":11,"toc":103},"minimark",[12,16,21,24,27,31,34,47,51,63,66,78,81,85,96],[13,14,15],"p",{},"Shipping yet another project management tool meant answering two questions: where to put AI, and where not to. FlowBit is the product of those two decisions.",[17,18,20],"h2",{"id":19},"why-another-pm-tool","Why another PM tool",[13,22,23],{},"Most teams using Jira, Linear, or Asana kept saying the same thing: the tool can do too much, and the team does too little of it. Complexity slows small teams down and forces large teams to rewrite their own processes.",[13,25,26],{},"When designing FlowBit we prioritized: few but right features, and AI assistance exactly where users touch the product daily.",[17,28,30],{"id":29},"why-mcp-matters","Why MCP matters",[13,32,33],{},"MCP (Model Context Protocol) creates a standard bridge between AI models and applications:",[35,36,37,41,44],"ul",{},[38,39,40],"li",{},"Claude, GPT-4, or a local model (Ollama) connect through one integration.",[38,42,43],{},"The model can see in-app context (team, sprint, ticket) without separate system prompts.",[38,45,46],{},"Data scoping is explicit — the model only reads what's allowed.",[17,48,50],{"id":49},"what-we-observed-in-the-field","What we observed in the field",[13,52,53,54,58,59,62],{},"After three months with pilot teams, the clearest finding: AI adds the most value not at the ",[55,56,57],"em",{},"deciding"," moment, but at the ",[55,60,61],{},"remembering"," one.",[13,64,65],{},"A typical flow:",[67,68,69,72,75],"ol",{},[38,70,71],{},"Meeting notes get transcribed.",[38,73,74],{},"FlowBit suggests likely tasks and owners from the text.",[38,76,77],{},"Sprint reports — who did what, where it stalled — drop into Slack automatically.",[13,79,80],{},"None of these steps is \"smart\" individually. Together, they erase the team's \"we forgot about that\" moments.",[17,82,84],{"id":83},"what-we-learned","What we learned",[35,86,87,90,93],{},[38,88,89],{},"Auto-suggested tasks must always pass through human approval. AI shouldn't create tickets alone.",[38,91,92],{},"Reports should be short. Anything past 8 lines doesn't get read.",[38,94,95],{},"Local mode (Ollama) is in demand — especially in the public and healthcare sectors.",[13,97,98,99,102],{},"FlowBit is still early. But positioning AI as a ",[55,100,101],{},"reminder"," rather than an accelerator is working.",{"title":104,"searchDepth":105,"depth":105,"links":106},"",3,[107,109,110,111],{"id":19,"depth":108,"text":20},2,{"id":29,"depth":108,"text":30},{"id":49,"depth":108,"text":50},{"id":83,"depth":108,"text":84},"FlowBit","2026-05-08","The useful part of AI-assisted task creation is making it easier for the team to decide where to focus. FlowBit's design choices and what we observed in the field.",false,"md",{},true,"\u002Fen\u002Fblog\u002Fflowbit-mcp-ile-otomatik-gorev-yonetimi",6,{"title":7,"description":114},"en\u002Fblog\u002Fflowbit-mcp-ile-otomatik-gorev-yonetimi",[124,125,126,127],"ai","mcp","product management","automation","eEPaJPHkXnNQIk4MQTX7y9-HiSS6VIOKRQGV8lN51P0",1781523777247]