A web developer shares experiences building systems around AI coding agents for reliable development and seeks advice on workflows and orchestration tools.
I’m a web developer and I use AI coding agents daily. At this point, getting an agent to write code isn’t really the problem anymore. The hard part is building everything around it so that it can actually work reliably. Over the last few months I’ve built a small system around my projects with: a knowledge base for each repo; reusable skills/rules containing my conventions; automated onboarding for local environments; a structured issue → development → verification → completion workflow; mandatory checks before a task can be considered done. The goal is for the agent itself to be replaceable. What should remain is the system around the agent: project knowledge, rules, guardrails, verification and workflow. The problem is that my current setup works, but it’s still cumbersome: onboarding isn’t always deterministic, context grows too much, rules start overlapping, and I still need too much manual intervention. So my main question is: How are you structuring this layer in real production projects? I’m particularly interested in approaches, repos, frameworks, skills or processes worth studying to make agentic development reliable, repeatable and maintainable. I can find endless discussions about which coding agent is better. I find much less about how to build a solid engineering system around the agent. There’s also a second problem I’m trying to solve. Is there any software that acts as a real control panel for this kind of workflow? What I have in mind is something that lets me: manage multiple GitHub repositories from one place; see issues/tasks across projects; launch or assign tasks to different coding agents; run multiple tasks in parallel; keep each task isolated in its own branch/worktree/workspace; see what each agent is currently doing; review progress, output, commits and pull requests; keep GitHub Issues as the source of truth; avoid being locked into a specific agent or model. Basically, I’d like a control plane that sits above GitHub and coding agents: issue → task → agent → isolated workspace → verification → commit/PR → done Preferably something local, open-source and agent-agnostic. Does something like this already exist and work well in practice, or are people mostly building their own orchestration layer?
A developer asks for recommendations on production orchestration tools for multi-agent AI workflows with branching, retries, and human-in-the-loop approvals, as their current FastAPI-based solution has become unmaintainable.
A discussion about deploying multi-agent AI systems in production, where different agents handle planning, execution, communication, and project management, asking about real-world experiences and bottlenecks.
A proposed workflow for AI coding agents that emphasizes brainstorming and boundary enforcement before code editing, seeking community feedback on its utility.
A discussion on the challenges of testing non-deterministic AI agents, questioning how developers validate tool usage, behavior, and multi-step workflows without traditional testing patterns.
A developer shares an experiment with an AI agent-based automation platform that builds and manages deterministic workflows, seeking feedback from the community.