The Gap in Enterprise AI
Enterprise AI orchestration has no middle ground. Teams either prototype with fragile low-code tools and rewrite for production, or build everything from scratch using AI libraries and frameworks.
EDDI Fills This Void
EDDI is a deployable middleware platform, not a library. It provides everything teams need out of the box:
- Visual Management UI β The EDDI Manager for building and monitoring agents
- Configuration-as-Code β Agent logic is JSON, not compiled code
- 42 MCP Tools β Full AI-native control via Model Context Protocol
- Enterprise Security β OIDC, vault, audit trails, no eval()
- Production Infrastructure β REST APIs, conversation state management, Prometheus metrics
- Horizontal Scaling β NATS JetStream for distributed architectures
Who Should Use EDDI?
- Enterprise teams who need a production-ready AI orchestration platform
- Prompt engineers who want to iterate without redeployment
- Regulated industries that require audit trails and EU AI Act compliance
- Platform teams β building internal AI services for multiple departments
EDDI vs. Typical Agent Frameworks
| Dimension | Python/Node Frameworks | EDDI |
|---|---|---|
| Concurrency | GIL or single-threaded event loop | Java 25 Virtual Threads β true OS-level parallelism |
| Agent Logic | Embedded in application code | Versioned JSON configs β update behavior without redeployment |
| Security Model | Relies on sandboxed code execution | No dynamic code execution; envelope-encrypted vault, SSRF protection |
| Compliance | Requires custom implementation | GDPR, HIPAA, EU AI Act infrastructure built-in |
| Audit Trail | Application-level logging | HMAC-SHA256 immutable ledger with cryptographic agent signing |
| Deployment | pip/npm + manual infrastructure | One-command Docker install, Kubernetes/OpenShift-ready |
12 LLM Providers Supported
Connect to any major LLM provider β or bring your own via any OpenAI-compatible endpoint.
| Category | Providers |
|---|---|
| Cloud APIs | OpenAI Β· Anthropic Claude Β· Google Gemini Β· Mistral AI |
| Enterprise Cloud | Azure OpenAI Β· Amazon Bedrock Β· Oracle GenAI Β· Google Vertex AI |
| Self-Hosted | Ollama Β· Jlama Β· Hugging Face |
| Compatible | Any OpenAI-compatible endpoint (DeepSeek, Cohere, etc.) via baseUrl |
8 Questions Every CIO Should Ask
When evaluating AI agent orchestration platforms, these are the questions that separate production-grade infrastructure from fragile prototypes:
Total Cost of Ownership: Build vs. Deploy
The hidden cost of using AI libraries is not the library itself β it's the invisible infrastructure teams must build, maintain, and secure around it:
Building with Libraries
- Custom REST API layer (2β4 weeks)
- Authentication & RBAC system (2β3 weeks)
- Conversation state persistence (1β2 weeks)
- Audit trail & compliance logging (2β4 weeks)
- Management UI for non-developers (4β8 weeks)
- Secret management integration (1β2 weeks)
- Horizontal scaling & coordination (2β4 weeks)
- Ongoing maintenance & security patching
Deploying EDDI
- One-command install (5 minutes)
- All of the above included out of the box
- Team focuses on business logic, not infrastructure
- Maintained by an 18-year-old open-source project
The Business Case
EDDI's value is measured in what teams don't have to build: the REST APIs, authentication systems, audit infrastructure, management UIs, and compliance tooling that would otherwise consume months of engineering time. Model cascading alone can reduce LLM costs by 60β80% by routing simple queries to cheaper models β escalating to powerful models only when confidence is low.
For regulated industries, the cost equation is even clearer: the alternative to EDDI's built-in compliance infrastructure is a custom implementation covering GDPR, EU AI Act, HIPAA, and potentially 15+ additional regulatory frameworks β each requiring its own data subject rights implementation, audit trail, and governance tooling.