Architecture Preview
Real-time model compile representation of your customized neural agent.
NEXUS-01
Coaching Institutes
blueprint_config.json READY
"agent_id": "nexus-01-agent",
"model_class": "deepseek-r1-mix",
"orchestration": {
"max_depth_loops": 15,
"fallback_model": "gemini-1.5-flash"
},
"tools": ["database", "whatsapp"],
"security": {
"guardrails_level": "strict",
"gvisor_sandbox": true
}
Understanding the Generated YAML Schema
The output YAML configuration describes the active nodes, tool permissions, and routing ceilings that represent your agent's neural structure. This file is parsed by our Node.js runtime to instantiate isolated runtime threads.
Multi-Agent RAG Pipeline Topology
Our autonomous swarm operates in a containerized pipeline, enriching prompts with real-time database contexts before LLM reasoning cycles occur.
1. Input Stream
User message, WhatsApp webhook, or form submit.
2. RAG Retrieval
Embed query & search MySQL/Pinecone vector space.
3. LLM Reasoning
Llama 3.3 70B synthesizes reply with context.
4. Guardrail Filter
PII scrubbers & safety constraints check output.
5. Dispatch Router
WhatsApp dispatch, CRM update, or system alert.
Orchestrators vs Executors
Swarm Orchestrators
High-level reasoning units (usually running Claude 3.5 or Deepseek-R1) that parse user intent, construct a plan, and route tasks to specialized sub-agents.
Executor Agents
Task-specific workers that execute exact tool calls—like performing a MySQL insert or sending a PDF attachment via SMTP—without full context of the larger goal.
Loop Ceilings & Cost Protection
To prevent infinite loops (where two agents repeatedly call each other due to a validation error), every blueprint enforces a strict "Loop Ceiling." Once an execution thread hits the loop limit, the node immediately pauses and escalates to a human operator, saving API token usage.
LLM Runtimes Selection
| Provider Model | Execution Type | Best Fit Use Case |
|---|---|---|
| Llama-3-70b (Groq) | Streaming LPU Execution | Real-time chat qualification |
| Gemini-1.5-Pro (Google) | Native RAG Context | Curriculum PDF searches |
Compliance & Isolation Levels
STRICT
Every database insert requires an encrypted double-validation token check. Zero PII storage.
STANDARD
Data is stored in standard relational databases with regular PII masking sweeps.
AUDIT ONLY
Agents read records and output logs to file systems without modifying database structures.
Self-Correcting Orchestration Loop
Our orchestrators execute tool calls inside a continuous validation cycle. If a validation rule fails, the agent self-corrects and retries dynamically.
Blueprint FAQs
Can I export the YAML blueprint?
Yes. Once you compile your configuration, you can download the raw schema file to run it locally inside our Node.js runtime service.
How do I update tools in a live agent?
Simply update the tools list in the agent's schema file and upload it to the admin console. The runner dynamically updates tool configurations without downtime.
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