Over the last few months, while working with AI tools in real projects, we kept running into the same limitation:

Most AI assistants work well for single prompts, but once tasks become multi-step or project-level, things start breaking down — context loss, inconsistent outputs, and no clear way to understand why something happened.

We initially tried stitching things together with prompts and scripts, but it quickly became fragile.

So we built AutomatosX to solve this internally.

The idea wasn’t to build another chat interface, but to focus on orchestration — planning tasks, routing work through the right agents, cross-checking outputs, and making everything observable and repeatable.

What AutomatosX currently focuses on:

Specialized agents (full-stack, backend, security, DevOps, etc.) with task-specific behavior

Reusable workflows for things like code review, debugging, implementation, and testing

Multi-model discussions, where multiple models (Claude, Gemini, Codex, Grok) reason together and produce a synthesized result

Governance & traceability, including execution traces, guard checks, and auditability

Persistent context, so work doesn’t reset every session

A local dashboard to monitor runs, providers, and outcomes

One thing we learned quickly is that orchestration matters more than prompting once AI is used for real development work. Reliability, explainability, and repeatability become far more important than raw model capability.

AutomatosX is open-source and still evolving. If anyone is curious, the repo is on github: https://github.com/defai-digital/AutomatosX

I’d really appreciate feedback from others who are building or using agent-based systems:

How are you coordinating agents today?

What’s been the hardest part to make reliable?