Hey folks 👋

I’ve been working on an AI agent platform called Noevex, focused on real production use—not just demos.

In practice, AI systems struggle with:

• multi-step orchestration
• connecting multiple data sources
• controlling agent actions
• debugging & trust

🚀 What is Noevex?

A full-stack platform to build, run, and control AI agents in production

Includes:

• Genesis → LLM foundation (hybrid models)
• Helion → orchestration (planning, memory, execution)
• Prism → multi-source retrieval
• Iris → governance (access + policy control)
• Argus → observability (tracing/debugging)
• Visor → UI

🧠 Prism (beyond basic RAG)

Instead of:

query → docs → answer

We do:

query → plan → retrieve (SQL + logs + metrics + vector) → correlate → rerank → suggest action

Example:

“Users can’t access websites”

• check metrics
• analyze logs
• find config change
• match past incidents
• retrieve runbook
• suggest fix

🔐 Iris (critical layer)

Agents don’t just answer—they act:

• restart services
• push configs
• query DBs

Most systems log after execution.

👉 Real need: control before execution

Iris provides:

• agent → tool → env permission control
• approval flows (HITL)
• audit + replay

⚙️ Flow

Prism → insight
Helion → orchestration
Iris → validation
Human → approval
Helion → execution
Argus → tracing

🤔 Why this?

• RAG = document retrieval
• Real systems = multi-source + actions + risk

Missing pieces:

• cross-system retrieval
• orchestration
• governance

❓ Curious:

• Are you going beyond RAG?
• How are you doing multi-source retrieval?
• Do you control agent execution or just observe it?

Would love feedback 🙌