Show HN: Open sourcing our ERP (Sold $500k contracts, 7k stars)

I’ve been contributing to Hive over the past couple of weeks, mostly around agent design patterns, integrations, and production-readiness.

What was the issue? Hive has powerful agent primitives, but early on there were few concrete reference patterns showing how to apply them to real-world, multi-step workflows.

How did I address it? I contributed by proposing and designing reference agent pipelines (e.g. multi-agent content research workflows) and scoped integrations focused on production use cases like security automation, scheduling, and external systems.

Why does it matter? Clear reference agents and narrowly scoped integrations make it much easier for teams to move from experimentation to real business workflows, which is where agent frameworks tend to break down.

Happy to answer questions or dive deeper into any of the designs.

I grew up helping my dad run his factory so I'm very familiar with ERP systems for manufacturing. A few years ago, when I decided to build a startup and I indentified the biggest problems with ERP is the fact they all just serve as data integration and system of records now - there're not enough processes automation. Therefore, I thought it'd be very meaningful to leverage AI to automate business processes such as PO, Price Requisition, and invoices, etc.

3 years in, I realized that every customer in our space (construction and contracting) want process automation, however, AI is simply not good enough - it's too slow, unpredictable, inconsistent, and overall hard to count on. For example, automating a quote by asking AI to insert dynamic variables from a relational database is hit or miss. Asking voice AI to provide training does not capture full breadth of the offline activities. Asking AI to fill out a work order creates a ton of errors.

Later, we decided that though LLM and the foundation models were progressing fast, the dev tools were lacking way behind, particular behind all the hypes and promises these AI applications claimed. The agents are not reliable, consistent, intelligent, evolving, and chances are the market would demand more apps to keep the party going.

Therefore, we went full open-source. The mission we have in mind is really to "generate reliable agents that can run business processes autonomously". We see all this hype about general computer use (GCUs) and can't help but making an opposing argument - that the AI agents need guardrails, more defined paths, and most importantly consistent results just like a human would need

- Proactive Reasoning (anticipating future needs or consequences)

- Memory & Experience (events affecting himself/herself)

- Judgment (based on experience)

- Tools & Skills (capabilities to execute)

- Reactive Adaptiveness (handling immediate roadblocks)

- Contextual Communication (articulating intent and collaborating with others)

- Character & Traits (consistent behavioral biases: Risk profile, Integrity, Persistence)

The project seems to have gained a bit of a traction so far and I hope that you can fork it and tell the community what's missing and what we should be working on. I deeply thank you because the it's truly painful to build and deploy these one-off agents that don't get utilized. (https://github.com/adenhq/hive).