Build custom AI agents that understand your codebase, workflows, and team standards. Share org-wide to multiply productivity. Access 100+ integrations, deploy in CI/CD, and tap into our open-source marketplace.
This is the 3rd launch from Zencoder . View more
Zenflow by Zencoder
Launching today
Zenflow streamlines Al-first engineering with specification-driven workflows, parallel agents, and built-in verification - so you can ship production-grade software.
Free Options
Launch Team / Built With
Zencoder
🚀 Hey Product Hunt!
Andrew here. While building our IDE extensions and cloud agents, we kept running into the same problem many of you probably face when using coding agents in complex repositories: agents getting stuck in loops, over-apologizing, and burning time without making real progress.
We tried to paper over this with scripts, but juggling terminals and copy-paste prompting quickly became painful. So we built Zenflow - a free desktop tool for orchestrating AI coding workflows.
It handles the things we kept missing in standard chat interfaces:
Dynamic Workflows: Workflows are defined in simple .md files, and agents can dynamically rewire the next steps based on what they discover mid-execution.
Spec Driven Development: Use formal specs to guide agents, ensuring the implementation matches your architectural intent before a single line of code is written.
Cross-Model Verification: Have Codex review Claude’s output, or run multiple models in parallel to see which one handles a specific codebase or task best.
Blast Mode (Multi-Model Inference): Run up to four different models (Claude, GPT, Gemini, Codex) on the same task simultaneously. Compare their outputs side-by-side and pick the best result.
Parallel Execution: Run multiple approaches on the same backlog item simultaneously mixing human-in-the-loop workflows for hard problems with faster “YOLO” runs for simpler tasks.
Project-Level Kanban: Track and manage all agent work through project lists and kanban-style views, not scattered terminal windows.
What we learned building Zenflow
After running 100+ experiments on SWE-Bench and private datasets, we found that models are increasingly overfit to public benchmarks. Real-world success doesn't come from "smarter" models alone; it comes from the "Goldilocks" Workflow just enough structure to prevent loops without over-orchestrating the creativity out of the AI.
We’ve been dogfooding this heavily to build our own IDE extensions, and we’d love to hear how it handles your toughest repos.
Zenflow is free to use and currently supports Claude Code, Codex, Gemini, and Zencoder.
Exciting launch, crew!
You guys are really taking spec-driven development to the next level. I'm excited to see how it pans out. Let's gooo!
Zencoder
@vibor_cipan Thank you Vibor! Do try the product and share your feedback!
Multi-agent “blast mode” and dynamic rewiring is powerful, but at scale the pain is non-determinism: agents race, loop, and produce conflicting diffs that are hard to replay or audit.
Best practice is a reproducible execution harness: sandboxed per-agent workspaces/branches, deterministic step graph with idempotent tools, and mandatory verify gates (lint + minimal tests) before merge, with full traces for replay.
How does Zenflow represent and version the workflow state, and can it enforce conflict-free patch application plus automatic rollback when verification fails?
Zencoder
@ryan_thill that’s a really good question and honestly, that’s exactly the class of problems Zenflow is designed to avoid. The way we think about it is: we don’t try to make multi-agent work “magically deterministic.” Instead, we make it observable, isolated, and auditable.
Concretely, every agent in Zenflow runs in its own fully sandboxed workspace. It’s a full copy of the repo with its own Git branch. So agents never race on files, they never overwrite each other, and you never get conflicting diffs produced at the same time. That whole class of non-deterministic merge issues just doesn’t happen.
On the workflow side, everything the agent does is driven by an explicit step graph things like requirements, spec, planning, implementation, verification. That state is materialized as real artifacts like spec.md plans, and diffs, and you can see and edit it at any point. If an agent decides to change the plan mid-flight, that change is visible and versioned as well.
For verification, we don’t auto-merge anything. You can add explicit verify steps run tests, lint, compile and you can also have another agent review the output before you move forward. If verification fails, nothing gets applied, because it’s still just a sandboxed branch.
Because of that model, we don’t really need rollback in the traditional sense. If something fails verification, you just discard that branch and move on main is untouched.
So the short version is: Zenflow enforces conflict-free execution by isolation, keeps workflows reproducible by making state explicit, and avoids chaos by never auto-applying changes. You get parallel exploration, but with the same safety properties you’d expect from a disciplined engineering process.
"Ads-for-All" eBook by TextCortex
Huge congrats! Zenflow’s multi-agent orchestration and built-in verification feels like a game-changer for scalable AI engineering. I'm excited to try real workflows beyond vibe coding. Good luck!