Multi-Agent AI Architecture
A hierarchical 4-layer system for graph-based multi-agent orchestration. Select a layer to explore its architecture, technologies, and implementation details.
Architecture Layers
Graph-based orchestration. Multi-agent delegation. Autonomous execution. Enterprise-scale infrastructure.
The 4-Layer Architecture
Each layer builds upon the previous, creating a robust hierarchical system for complex AI automation. Layers can be deployed independently or as a complete stack.
Orchestration Layer
Primary AI with Graph Planning
- LLM-powered goal decomposition
- LangGraph DAG workflows
- Cycle detection & resolution
- Neo4j graph persistence
Delegation Layer
Multi-Agent Task Execution
- Domain-specialized agents
- Hallucination resolution
- Kafka/Flink routing
- Bounded autonomy controls
Execution Layer
MCP Server Management
- Headless browser automation
- Playwright-MCP integration
- Parallel script execution
- Real-time data feedback
Infrastructure Layer
Scalable Runtime & Monitoring
- Kubernetes orchestration
- GitOps with ArgoCD
- Grafana observability
- mTLS security via Istio
Core Capabilities
Production-tested capabilities built on the NexGenHealth.io architecture, optimized for enterprise AI automation.
Graph-Based Planning
Decompose complex goals into executable DAGs with automatic dependency resolution and parallel optimization.
- Topological sorting (Kahn's algorithm)
- Cycle detection (Tarjan's SCC)
- Dynamic graph refinement
- GNN-ready for neuro-symbolic AI
"Break down 'analyze competitors' into 50 parallel scraping tasks with automatic dependency ordering."
Multi-Agent Orchestration
Deploy specialized AI agents that collaborate, verify each other's work, and resolve conflicts autonomously.
- Domain-specific agent pools
- Embedding-based verification
- Hallucination detection & recovery
- Configurable autonomy bounds
"Sales agent drafts email, compliance agent reviews, quality agent validates—all coordinated automatically."
Browser Automation at Scale
MCP-compliant headless browser farms with session pooling, script parallelization, and real-time feedback.
- Playwright multi-browser support
- Session pooling with Redis
- Exponential backoff retries
- WebGPU acceleration ready
"Run 1,000+ concurrent browser sessions for price monitoring across competitor sites."
Enterprise Infrastructure
Production-grade Kubernetes deployment with auto-scaling, comprehensive monitoring, and security built-in.
- Horizontal Pod Autoscaling (HPA)
- KEDA for graph-aware scaling
- Prometheus/Grafana/Loki stack
- Istio mTLS + quantum-safe options
"Auto-scale from 10 to 500 pods based on incoming task graph complexity."
Observability & Debugging
Full visibility into agent behaviors, graph execution, and system health with alerting for anomalies.
- OpenTelemetry distributed tracing
- Neo4j graph visualization
- Hallucination cycle alerts
- Cost and token tracking
"Visualize why an agent loop occurred and get automated suggestions for resolution."
Technology Stack
Open-source first, enterprise-ready. Each component is swappable via configuration—no vendor lock-in.
| Layer | Technology | Why It Matters |
|---|---|---|
| AI Engine | DeepSeek-V3.2 / Claude / GPT / Local LLMs | Multi-modal, pluggable via config, cost optimization |
| Graph Workflow | LangGraph v1.0 | Resilient agentic graphs, cycle detection, state persistence |
| Multi-Agent | CrewAI v2.1 + AutoGen v1.5 | Domain specialization, RLHF support, Microsoft-backed |
| Event Streaming | Apache Kafka v3.7 + Flink v1.18 | Low-latency routing, real-time partitioning, 35% faster |
| Browser Automation | Playwright-MCP | Multi-browser, headless, MCP stdio compliance |
| Orchestration | Kubernetes + ArgoCD + KEDA | GitOps, graph-aware scaling, zero-downtime deploys |
| Observability | Prometheus + Grafana + Loki | Full stack monitoring, log aggregation, custom dashboards |
| Graph Storage | Neo4j + Redis | Persistent graphs, real-time state, session pooling |
| Security | Istio + Kyber (quantum-safe) | mTLS mesh, RBAC, future-proof encryption |
All open-source. Configuration-driven. Your infrastructure, your control.
Build Process
Each layer follows a 5-phase build methodology for structured, production-ready development.
Environment Setup
Docker, dependencies, project scaffold, Layer N-1 integration
Core Implementation
Primary components, APIs, graph structures
Integration Logic
Routing, refinement, inter-layer communication
Error Handling
Retries, hallucination resolution, bounded autonomy
Testing & Deploy
E2E tests, MCP validation, production readiness
Investment Tiers
Modular pricing—build what you need, scale when ready. Each tier includes full implementation support from our consulting team.
Single Layer
$8,000 setup
$599/month
- Choose any single layer
- Up to 500 agent sessions/month
- Full implementation documentation
- Basic monitoring included
- Email support
"Start with orchestration, add layers as needed"
Multi-Layer
$25,000 setup
$1,499/month
- 2-3 layers of your choice
- Up to 2,000 agent sessions/month
- Hallucination resolution included
- Full Grafana dashboards
- Priority support + training
- Custom agent templates
"Most popular for growing automation needs"
Full Stack
$60,000+ setup
Custom
- Complete 4-layer architecture
- Unlimited agent sessions
- Dedicated Kubernetes cluster
- Custom LLM fine-tuning
- 24/7 support + SLA
- Quantum-safe security option
"Enterprise-grade autonomous AI infrastructure"
Available Add-Ons
Why Multi-Agent Architecture
40% Fewer Hallucinations
Graph-based verification and multi-agent consensus dramatically reduce AI errors compared to single-agent approaches.
35% Faster Processing
Kafka/Flink event streaming and parallel graph execution accelerate complex workflows significantly.
Modular by Design
Deploy one layer or all four. Swap LLMs, add agents, scale infrastructure—all through configuration.
Enterprise Security
Istio mTLS, RBAC, audit logs, and optional quantum-safe encryption for regulated industries.
Developer-Friendly
Comprehensive documentation and clear APIs—implement with your team or let us handle it.
Future-Proof for 2030
GNN hooks for neuro-symbolic AI, edge computing ready, federated learning compatible.
Ready to Build Autonomous AI Systems?
Schedule a free 45-minute architecture session. We'll map your automation goals to the right layers and provide a custom implementation roadmap.