🐉 Daedalus Systems — Explainable AI Infrastructure

Daedalus Systems is an enterprise platform for building, deploying, and governing symbolic + LLM hybrid AI systems. It enables organizations to codify their internal reasoning processes into secure, auditable logic — transforming opaque AI decisions into transparent, traceable outcomes.

Daedalus is built atop the Feather Agent Framework and uses Daedalus Cloud, Feather Runtime, and Daedalus Connectors to orchestrate symbolic reasoning, LLM comparison, and reinforcement learning in real-time.

🧠 Mission

Daedalus's mission is to make enterprise AI explainable, secure, and composable — where every decision can be audited, reasoned about, and improved.

Most AI pilots fail because they lack:

• Explainability — Can't understand how decisions are made
• Governance — No oversight or compliance controls
• Integration — Doesn't work with existing systems

Daedalus solves this by combining symbolic AI (explicit logic) with LLM reasoning (pattern-based inference) inside a unified drag-and-drop environment.

⚙️ Core Components

| Component | Description | |------------|-------------| | Daedalus Cloud | SaaS control plane and visual builder (Next.js + ReactFlow) | | Feather Runtime | Customer-side execution engine (Node.js / Docker) | | Daedalus Connectors | Secure adapters for APIs, databases, and systems | | Symbolic Engine | JSONLogic-based interpreter with audit trace | | RL Evaluator | Compares LLM outputs vs. symbolic truth for reinforcement | | Audit Layer | Cryptographically signed trace logs for compliance |

🧩 Architecture Overview

graph TB
    A[User] --> B[Daedalus Cloud Frontend]
    B --> C[Visual Flow Builder]
    C --> D[Flow Specification]
    D --> E[Feather Runtime]
    E --> F[Symbolic Engine]
    F --> G[Daedalus Connectors]
    G --> H[Customer Systems]
    F --> I[Audit Logger]
    I --> J[Trace Viewer]
    J --> B
    E --> K[RL Evaluator]
    K --> L[Model Comparison]
    L --> B

Daedalus Cloud never handles raw customer data — only metadata and hashes of symbolic evaluations.

🧰 Quick Start

Prerequisites

• Node.js ≥ 20
• Docker & Docker Compose
• pnpm (preferred) or npm/yarn
• PostgreSQL (for audit logs)

Clone Repository

git clone https://github.com/Geddydukes/daedalus-sdk.git
cd daedalus-sdk
pnpm install

Run Local Development Stack

# Launch Daedalus Cloud
pnpm run dev

# Launch Feather Runtime (sandbox)
docker compose up feather-runtime

Visit http://localhost:3000 to access Daedalus Cloud.

🚀 SDK Documentation

New: The Daedalus SDK is now available for building symbolic AI agents!

• SDK Quick Start — Get started in 5 minutes
• Full SDK Documentation — Complete API reference
• SDK Quick Start Guide — Minimal examples

📂 Complete Documentation

All comprehensive documentation is organized in the /docs folder:

Core Platform Documentation

• Compatibility Layer — Unified compatibility layer & frontend integration
• Framework Customizations — Unified framework architecture & frontend integration
• Platform Architecture — Complete platform architecture & extensions
• Implementation Guide — Complete implementation guide with phases
• Migration Plan — Complete platform migration plan
• Testing Strategy — Comprehensive testing strategy

Frontend Documentation

• Frontend Development Plan — Frontend development phases and roadmap
• User Flow — Complete user journey and workflow
• Frontend System Architecture — Frontend system architecture
• Frontend README — Frontend overview and quick start

Framework Documentation

• API Reference — Feather-agent API reference
• Deployment — Deployment guide
• Examples — Usage examples
• Quick Start — Quick start guide

📖 View Complete Documentation Index

🔧 Example Workflow

1. Create a Flow

flow:
  - fetch: crm.getCustomerData
  - rule: DSCR_MIN_1_20
  - if_pass: notify("Loan pre-approved")
  - if_fail: escalate("Manual review")

2. Register a Connector

docker run Daedalus-connector --token=$Daedalus_TOKEN

3. Execute a Test

curl -X POST localhost:8080/run \
  -H 'content-type: application/json' \
  -d '{"noi": 120000, "debt_service": 100000}'

Response:

{
  "verdict": "PASS",
  "dscr": 1.2,
  "trace": ["Rule DSCR_MIN_1_20 met threshold"],
  "timestamp": "2025-10-20T18:22Z"
}

🔒 Security Model

| Layer | Responsibility | Mechanism | |-------|---------------|-----------| | Daedalus Cloud | Control plane only | TLS 1.3, OAuth2, RBAC | | Feather Runtime | Execution sandbox | Docker isolation, ephemeral volumes | | Connectors | Scoped data access | JWT auth, local logging | | Audit Layer | Trace integrity | SHA-256 signed hashes |

Sensitive data never leaves the customer's infrastructure.
Daedalus Cloud stores only job IDs, rule versions, and execution summaries.

🧠 Symbolic AI + LLM Reinforcement

Daedalus integrates LLM evaluation loops that compare symbolic outputs with generative model predictions:

1. Symbolic reasoning executes ground-truth rules
2. LLM predicts an outcome for the same case
3. Disagreements logged as preference pairs
4. Reinforcement Learning (RLAIF or DPO) fine-tunes the model
5. Over time, LLM accuracy converges toward the symbolic baseline

This enables machine-verifiable reasoning that improves continuously.

📊 Observability

Daedalus emits OpenTelemetry-compatible structured logs:

{
  "job_id": "abc123",
  "rule_id": "DSCR_MIN_1_20", 
  "result": "PASS",
  "value": 1.23,
  "runtime": 45,
  "timestamp": "2025-10-20T10:45:12Z"
}

Supports exporters for:

• Datadog
• Grafana
• ELK Stack
• S3 / Glacier Archival

🧱 Repository Structure

daedalus-sdk/
├── apps/
│   ├── Daedalus-cloud/         # Next.js SaaS frontend
│   └── feather-runtime/     # Node.js runtime engine
├── packages/
│   ├── symbolic-engine/     # JSONLogic interpreter + DSL compiler
│   ├── connectors/          # SDKs for APIs & databases
│   └── shared/              # Utils, schema validators, types
├── docs/                    # Complete documentation
├── tests/                   # Test suites
├── docker-compose.yml
└── README.md

🧩 Deployment Options

| Mode | Description | Example Users | |------|-------------|---------------| | SaaS | Daedalus Cloud hosts control plane; runtime external | Startups, SMBs | | Hybrid | Cloud builder + on-prem Feather Runtime | Fintechs, Legaltech | | On-Prem | Full stack deployed internally | Healthcare, Banking |

Deployment managed via Docker, Kubernetes, or Terraform.

🧭 Roadmap

| Phase | Milestone | Target | |-------|-----------|--------| | Alpha (Q4 2025) | Symbolic engine + Cloud builder | ✅ Complete | | Beta (Q1 2026) | Hybrid deployment + audit system | In Progress | | v1.0 Launch (Q2 2026) | Marketplace + RL loop | Planned | | v2.0 (2027) | Multi-agent orchestration & self-optimizing rules | Planned |

🧩 Example Use Cases

• Finance: Loan eligibility & credit risk engines
• Legal: Contract clause validation
• Healthcare: Treatment protocol compliance
• Manufacturing: Safety check automation
• GovTech: Policy audit and decision justification

📈 Market Context

• MIT Tech Review (2025): "95% of GenAI pilots fail to deliver ROI."
• S&P Global (2025): "42% of firms abandon AI within a year due to poor governance."
• Gartner (2025): "60% of AI projects will be canceled without explainability."
• McKinsey (2025): "80% of enterprises will require AI audit layers by 2027."

Daedalus directly addresses these pain points by delivering explainable AI infrastructure as a product.

🤝 Contributing

Daedalus welcomes contributions from the open-source community.

Development

pnpm run dev
pnpm run test

Guidelines

• Use conventional commits (feat:, fix:, chore:)
• PRs must include updated tests and documentation
• Follow the Daedalus Code of Conduct

📜 License

Daedalus Systems © 2025 Geddy Dukes
Licensed under the Elastic License 2.0 (ELv2).
Commercial use available via Daedalus Cloud Enterprise.