Zenflow is a free desktop tool designed to orchestrate AI coding agents, specifically addressing issues like agents getting stuck in loops or producing 'you're right' responses. It enables cross-model verification, parallel execution of different approaches, and dynamic workflows configured via markdown files. The makers highlight learnings on benchmark saturation and the 'Goldilocks' zone for workflow complexity.
Reach solo founders and AI engineers using AI coding tools through relevant communities and demonstrate how Zenflow principles can be applied to enhance 'forge-kit' automation, 'mcp-kit' server building, 'agent-eval-lab' testing, and 'ai-usage-monitor' tracking.
Hi HN, I’m Andrew, Founder of Zencoder. While building our IDE extensions and cloud agents, we ran into the same issue many of you likely face when using coding agents in complex repos: agents getting stuck in loops, apologizing, and wasting time. We tried to manage this with scripts, but juggling terminal windows and copy-paste prompting was painful. So we built Zenflow, a free desktop tool to orchestrate AI coding workflows. It handles the things we were missing in standard chat interfaces: Cross-Model Verification: You can have Codex review Claude’s code, or run them in parallel to see which model handles the specific context better. Parallel Execution: Run five different approaches on a backlog item simultaneously—mix "Human-in-the-Loop" for hard problems with "YOLO" runs for simple tasks. Dynamic Workflows: Configured via simple .md files. Agents can actually "rewire" the next steps of the workflow dynamically based on the problem at hand. Project list/kanban views across all workload What we learned building this To tune Zenflow, we ran 100+ experiments across public benchmarks (SWE-Bench-*, T-Bench) and private datasets. Two major takeaways