The Linux-Native, eBPF-Powered Security & Governance Mesh for AI Agent Workloads
EnterpriseRAG-AI is an open-source AI infrastructure platform designed to make enterprise AI workloads secure, observable, and reliable.
Modern AI applications require more than powerful language models. Production AI systems need reliable retrieval, secure data boundaries, complete execution visibility, and infrastructure-level reliability.
EnterpriseRAG-AI provides an infrastructure layer for building and operating production-grade Retrieval-Augmented Generation systems and AI agent workloads.
The platform combines:
- Retrieval infrastructure
- Enterprise security controls
- Distributed observability
- AI execution diagnostics
- Reliability engineering workflows
into a unified AI operations platform.
Building reliable AI systems introduces several engineering challenges.
Traditional retrieval systems based only on semantic similarity can struggle with:
- Technical identifiers
- Exact keyword matching
- Enterprise terminology
- Structured information
- Compliance-heavy documents
EnterpriseRAG-AI focuses on improving retrieval quality through intelligent retrieval workflows, ranking strategies, metadata-aware processing, and contextual analysis.
Modern AI workloads are difficult to debug without complete execution visibility.
Teams need to understand:
- Which documents were retrieved
- How context was constructed
- Where latency was introduced
- How models executed requests
- Why failures occurred
EnterpriseRAG-AI introduces observability across the complete AI request lifecycle.
Enterprise AI systems require strong security boundaries.
EnterpriseRAG-AI focuses on:
- Tenant-aware access control
- Authentication workflows
- Secure document retrieval
- Audit visibility
- Policy-driven AI operations
Future: eBPF Kernel Telemetry Layer
EnterpriseRAG-AI focuses on building reliable retrieval workflows for enterprise AI applications.
Capabilities include:
- Semantic vector retrieval
- Hybrid retrieval workflows
- Document processing pipelines
- Context optimization
- Retrieval diagnostics
- Query analysis
- Metadata-aware retrieval
- Retrieval performance monitoring
Production AI systems require complete visibility across execution workflows.
EnterpriseRAG-AI provides observability capabilities across:
- Request lifecycle tracking
- Retrieval execution stages
- LLM execution flow
- Token usage monitoring
- Latency analysis
- Distributed tracing
- Infrastructure telemetry
Enterprise AI workloads require secure boundaries around data and execution.
The platform provides foundations for:
- Authentication and authorization
- Role-based access control
- Tenant-aware data isolation
- Audit logging
- Secure retrieval workflows
- Policy-driven AI operations
EnterpriseRAG-AI explores reliability patterns required for production AI infrastructure.
Engineering areas include:
- Async execution workflows
- Background processing systems
- Error handling mechanisms
- Retry strategies
- Failure diagnostics
- Infrastructure health monitoring
EnterpriseRAG-AI includes a unified operational dashboard designed for AI infrastructure management.
The control center provides a single interface to understand, monitor, and debug AI workloads.
Provides visibility into:
- Service health
- Active workloads
- Request activity
- Infrastructure status
- System performance
Provides insights into:
- Retrieval latency
- Retrieved documents
- Context quality
- Ranking information
- Query execution flow
Tracks:
- Distributed traces
- Request spans
- Backend execution stages
- Latency bottlenecks
- Infrastructure events
Provides:
- Security events
- Audit history
- Access monitoring
- Tenant activity
- Governance visibility
Visualizes:
- Service dependencies
- Worker status
- Database health
- Cache activity
- Infrastructure topology
Future analytics modules include:
- Token usage analysis
- AI workload cost insights
- Performance trends
- Usage patterns
EnterpriseRAG-AI is designed around infrastructure-level visibility.
Planned visualization systems:
Interactive visualization of:
- API Gateway
- Security Layer
- Retrieval Engine
- Vector Database
- Cache Layer
- LLM Gateway
- Workers
- Observability Components
A workspace for understanding:
- Query execution
- Retrieved chunks
- Ranking decisions
- Context assembly
- Retrieval quality
A debugging interface for:
- Request traces
- Execution spans
- Latency breakdowns
- Service dependencies
Monitoring for:
- Token consumption
- Request costs
- Usage trends
- Optimization opportunities
- FastAPI
- Python
- PostgreSQL
- Redis
- FAISS
- Celery
- React
- TypeScript
- Tailwind CSS
- Recharts
- React Flow
- OpenTelemetry
- Jaeger
- Prometheus
- Grafana
- Docker
- NGINX
- Kubernetes
- Vercel
| Area | Status |
|---|---|
| RAG Pipeline | Active |
| Retrieval Optimization | Active |
| Security Controls | Active |
| Observability Layer | Active |
| Enterprise Dashboard | In Progress |
| Retrieval Debugging Tools | In Progress |
| Distributed Trace Explorer | In Progress |
| Infrastructure Topology | In Progress |
| Token Usage Analytics | Planned |
| AI Agent Monitoring | Planned |
| eBPF Kernel Telemetry | Research |
| Advanced Governance Layer | Planned |
Architecture
Users
|
|
API Gateway Layer
|
|
Security & Tenant Control
(RBAC + Metadata Policies)
|
|
Retrieval Control Plane
|
-------------------------
| |
Vector Retrieval Cache Layer
FAISS Redis
|
|
Context Optimization
Token Management
Retrieval Pipeline
|
|
LLM Gateway
|
|
Observability Platform
|
---------------------------
| | |
OpenTelemetry Jaeger Prometheus/Grafana
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