What are the main problems developers face when building systems where multiple AI agents work together?
When building systems where multiple AI agents collaborate, I highlight three critical bottlenecks:Context Collisions and “Hallucinatory Resonance”: A situation where one agent generates a false premise, and the second agent builds upon it, amplifying the error exponentially.Orchestration and State Management: The difficulty of maintaining a coherent “shared memory” without over-inflating the context window or wasting tokens.
Infinite Loop Logic: The risk of agents entering a recursive cycle of clarifying questions instead of moving toward task execution.
I’ve been doing it for a long time. I’ve had zero issues. Have you been having problems or are you attempting to anticipate problems and create solutions for them?
I implemented what I’ve called a Cortex module. It essentially makes all the decisions and decides what bot does what. Meaning the model I talk to tells the other models what to do via tool calls. They all share 1 source of truth in memory.
Re: How does the NovBase orchestration handle multi-model conflicts?
Great question. What you see in the logs is the result of the Cortex Module deployment.
In NovBase, we’ve moved away from simple linear prompting. Cortex acts as the supreme decision-maker. It doesn’t just “chat”; it orchestrates a specialized swarm of models through high-level tool calls.
The Key Innovations:
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The Unified Source of Truth: All active models (Vision, Code-Analytic, and Reasoning cores) tap into a single, synchronized memory layer. There is no “hallucination lag” between agents because they share the same cognitive state in real-time.
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Cortex Dominance: Cortex evaluates the user’s intent and dictates specific roles. For example, if a query requires visual analysis, Cortex triggers the
llavanode, captures the metadata, and feeds it into the8BReasoning Core for final synthesis. -
Hardware Efficiency: This is running locally on consumer-grade hardware (Acer Nitro V15). By offloading specific tasks to smaller, optimized “Role Slots” (as seen in my config screenshot), we achieve near-zero latency without relying on massive cloud clusters.
The goal was to create a digital organism that doesn’t just simulate intelligence but actively manages its own sub-processes.
Cortex is the brain; the rest are the muscles.
Here is a live look at the current NovBase architecture deployment. It shows how the strike system maintains discipline within the intelligence swarm, and how the shared memory is structured across different specialized roles.
This single, local view is what orchestrates the entire RealityCortex process.
Hey @Pimpcat-AU and @Uzer-namo-2024 am I right in assuming that you guys work together (same name for the “primary” module, referencing an image in the other’s post) or is this just a massive coincidence. I’m building a personal solution extremely similar to what you’ve each laid out above with similar functional requirements (consumer-grade hardware) indeed the screenshot in @Pimpcat-AU 's post could have come from my own system ;-). I’m just asking because I’m curious what NovBase is - a commercial entity, an open-source library etc and I’d be interested in finding out more (to compare approaches)?
No. He’s just copy pasting stuff to and from a chat model.
@nick-brown, glad the NovBase architecture caught your eye.
To answer your question about “working together” — it’s quite simple. We aren’t a commercial entity; think of us as a private engineering group. What you’re seeing in the console is the work of an autonomous Swarm Intelligence system I’ve deployed locally using an 8b model.
The query was targeting the latest leaks for the iPhone 17 Pro Max (2026). Take a closer look at the log:
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T-Hunt (7.35s): A swarm of 8 units scouted the web, bypassed marketplace noise, and extracted 6.5k characters of “raw” technical intel.
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T-Synth (6.21s): The local core on my Nitro V15 processed this data and delivered the hard facts: a 4823 mAh battery, the A19 Pro chip, and new cooling specs (VC technology).
NovBase isn’t a library or a product. It’s a concept of “digital cloning” for analytical processes. The core (Cortex) controls the “muscles” (agents), while the strike system I’ve implemented keeps them in check to ensure they don’t bring back junk from SEO-bloated sites.
If you’re building something similar on consumer-grade hardware — respect. We could compare core performance; my current config hits a full “search-analyze-output” cycle in under 14 seconds.
If you’re building something similar on consumer-grade hardware — respect. We could compare core performance; my current config hits a full “search-analyze-output” cycle in under 14 seconds.
Public Proof of Concept:
You can review the high-density output sample here: NovBase-VTX/OSINT-Apple-Intelligence-Sample
[OPEN] NovBase OSINT Report (Swarm Sync: 6522 chars)