Autonomous AI Infrastructure Control System
Budget: $750 – $1,500 USD
Project Title
Autonomous AI Infrastructure Operating System with Multi-LLM Cross-Check, Skill Governance & Real-Time Dashboard
⸻
Project Summary
We are building a fully autonomous AI Infrastructure Control System for RTX (Ubuntu), controlled from macOS, using OpenClaw Skill architecture and strict multi-LLM governance.
This system must:
• Operate entirely via Skills (no raw bash outside framework)
• Generate multiple solution strategies before execution
• Cross-check each strategy and script using GPT and DeepSeek
• Perform deterministic safety validation before execution
• Execute via secure sandbox
• Auto-repair failures
• Stream terminal output in real time
• Integrate with Kestra, Codex CLI, and Xcode
• Provide a governance-level real-time dashboard
This is not a script.
This is an AI Infrastructure Control Plane.
⸻
Mandatory Multi-LLM Cross-Check Engine
Before any execution:
1. Generate minimum 3 solution strategies
2. Validate each via:
• GPT (reasoning validation)
• DeepSeek (technical validation)
3. Apply deterministic rule engine
4. Compute risk model per strategy
5. Select optimal strategy via weighted consensus
6. Execute only after approval threshold met
After execution:
• If failure detected:
• Parse logs
• Classify error
• Generate repair strategy
• Re-run full multi-LLM cross-check
• Retry (configurable cycle limit)
No blind execution allowed.
⸻
Solution Strategy Engine
The system must:
• Generate multiple solution approaches
• Simulate infrastructure impact
• Calculate:
• Risk score
• Downtime probability
• Reversibility index
• Present rejected strategies
• Adapt strategy if execution fails
This engine must be integrated into Task Tree and Dashboard.
⸻
OpenClaw Skill Architecture (JSON/YAML Based)
All actions must be implemented as Skills.
Each Skill must include:
• JSON or YAML contract
• Risk profile
• Permission declaration
• Input/output schema
• Dry-run support
• Rollback support
• Consensus requirement flag
• Simulation requirement flag
System must support:
• Skill generation engine
• Skill installer/remover
• Skill dependency graph
• Skill registry with versioning
• Sandbox testing before activation
⸻
Docker & Infrastructure Skills
Required skills:
• Docker orchestration
• Port conflict detection & auto-resolution
• Service repair
• Stack deploy/rollback
• Network recovery
• Volume backup & restore
Must support self-healing behavior.
⸻
Codex CLI Integration
Codex must be integrated as an autonomous Skill:
Capabilities:
• Code generation
• Refactoring
• CI error fixing
• Script improvement
• Repo-aware modifications
Codex must also pass multi-LLM validation before deployment.
⸻
Xcode Integration
System must include an Xcode Skill capable of:
• Creating/modifying Swift projects
• Generating files
• Parsing build logs
• Classifying compile errors
• Applying fixes via strategy engine
• Running tests
• Revalidating builds
All fixes must pass GPT + DeepSeek consensus before applying.
⸻
Kestra Integration
System must integrate with Kestra orchestration:
• Create / update / delete flows
• Monitor execution
• Trigger flows from Skills
• Insert AI decision logic into Kestra workflows
• Sync Task Tree with Kestra execution state
Kestra must become part of AI decision DAG.
⸻
Persistent Terminal Control (Mac RTX)
System must:
• Maintain persistent SSH sessions
• Stream stdout/stderr in real time
• Capture structured logs
• Provide bi-directional command channel
• Eliminate manual copy-paste of logs
Terminal execution must be observable inside Dashboard.
⸻
Governance Dashboard (Mandatory)
Web-based real-time AI Control Console.
Must include:
1. Global System Panel
• Health score
• Active jobs
• Repair cycles
• Risk index
• Consensus stability indicator
2. DAG-Based Task Tree
Each job visualized as dependency graph:
• Snapshot
• Strategy generation
• Scenario cross-check
• Execution
• Repair loop
• Validation
• Final state
Each node displays:
• Status
• Risk score
• LLM confidence
• Execution time
• Rollback availability
3. Multi-LLM Consensus Viewer
Per action:
• GPT verdict
• DeepSeek verdict
• Risk engine result
• Final decision
• Confidence %
4. Repair Loop Monitor
• Error classification
• Repair attempt count
• Strategy switching history
5. Digital Twin Viewer
Before/after diff:
• Containers
• Ports
• Services
• Env vars
• Config files
6. Policy & Governance Panel
Adjust:
• Risk thresholds
• Consensus level (2/3 vs 3/3)
• Dry-run mode
• Strict security mode
No raw log reading should be required to understand state.
⸻
Digital Infrastructure Twin
Before and after execution:
• Docker graph snapshot
• Port map
• Service dependency map
• Process tree
• Environment diff
Unexpected changes → rollback trigger.
⸻
Chaos Testing & Self-Healing
System must survive:
• Broken docker network
• Port conflicts
• Killed containers
• Permission denied
• Corrupted configs
And auto-repair via strategy engine.
⸻
Technical Stack Requirements
• Advanced Ubuntu systems knowledge
• Docker / Compose mastery
• SSH automation
• Python orchestration
• LLM API orchestration
• WebSocket real-time dashboard
• DAG workflow modeling
• Security-first execution sandbox design
• DevOps + ML hybrid experience
Senior / Architect level only.
⸻
Deliverables
• Complete autonomous system
• One-shot installer
• Skill registry framework
• Strategy engine
• Multi-LLM consensus engine
• Dashboard
• Kestra integration
• Codex integration
• Xcode integration
• Chaos test suite
• Documentation
• Demo video
• Source repository
⸻
Timeline
8–12 weeks realistic
⸻
Budget- 800$
Autonomous AI Infrastructure Operating System with Multi-LLM Cross-Check, Skill Governance & Real-Time Dashboard
⸻
Project Summary
We are building a fully autonomous AI Infrastructure Control System for RTX (Ubuntu), controlled from macOS, using OpenClaw Skill architecture and strict multi-LLM governance.
This system must:
• Operate entirely via Skills (no raw bash outside framework)
• Generate multiple solution strategies before execution
• Cross-check each strategy and script using GPT and DeepSeek
• Perform deterministic safety validation before execution
• Execute via secure sandbox
• Auto-repair failures
• Stream terminal output in real time
• Integrate with Kestra, Codex CLI, and Xcode
• Provide a governance-level real-time dashboard
This is not a script.
This is an AI Infrastructure Control Plane.
⸻
Mandatory Multi-LLM Cross-Check Engine
Before any execution:
1. Generate minimum 3 solution strategies
2. Validate each via:
• GPT (reasoning validation)
• DeepSeek (technical validation)
3. Apply deterministic rule engine
4. Compute risk model per strategy
5. Select optimal strategy via weighted consensus
6. Execute only after approval threshold met
After execution:
• If failure detected:
• Parse logs
• Classify error
• Generate repair strategy
• Re-run full multi-LLM cross-check
• Retry (configurable cycle limit)
No blind execution allowed.
⸻
Solution Strategy Engine
The system must:
• Generate multiple solution approaches
• Simulate infrastructure impact
• Calculate:
• Risk score
• Downtime probability
• Reversibility index
• Present rejected strategies
• Adapt strategy if execution fails
This engine must be integrated into Task Tree and Dashboard.
⸻
OpenClaw Skill Architecture (JSON/YAML Based)
All actions must be implemented as Skills.
Each Skill must include:
• JSON or YAML contract
• Risk profile
• Permission declaration
• Input/output schema
• Dry-run support
• Rollback support
• Consensus requirement flag
• Simulation requirement flag
System must support:
• Skill generation engine
• Skill installer/remover
• Skill dependency graph
• Skill registry with versioning
• Sandbox testing before activation
⸻
Docker & Infrastructure Skills
Required skills:
• Docker orchestration
• Port conflict detection & auto-resolution
• Service repair
• Stack deploy/rollback
• Network recovery
• Volume backup & restore
Must support self-healing behavior.
⸻
Codex CLI Integration
Codex must be integrated as an autonomous Skill:
Capabilities:
• Code generation
• Refactoring
• CI error fixing
• Script improvement
• Repo-aware modifications
Codex must also pass multi-LLM validation before deployment.
⸻
Xcode Integration
System must include an Xcode Skill capable of:
• Creating/modifying Swift projects
• Generating files
• Parsing build logs
• Classifying compile errors
• Applying fixes via strategy engine
• Running tests
• Revalidating builds
All fixes must pass GPT + DeepSeek consensus before applying.
⸻
Kestra Integration
System must integrate with Kestra orchestration:
• Create / update / delete flows
• Monitor execution
• Trigger flows from Skills
• Insert AI decision logic into Kestra workflows
• Sync Task Tree with Kestra execution state
Kestra must become part of AI decision DAG.
⸻
Persistent Terminal Control (Mac RTX)
System must:
• Maintain persistent SSH sessions
• Stream stdout/stderr in real time
• Capture structured logs
• Provide bi-directional command channel
• Eliminate manual copy-paste of logs
Terminal execution must be observable inside Dashboard.
⸻
Governance Dashboard (Mandatory)
Web-based real-time AI Control Console.
Must include:
1. Global System Panel
• Health score
• Active jobs
• Repair cycles
• Risk index
• Consensus stability indicator
2. DAG-Based Task Tree
Each job visualized as dependency graph:
• Snapshot
• Strategy generation
• Scenario cross-check
• Execution
• Repair loop
• Validation
• Final state
Each node displays:
• Status
• Risk score
• LLM confidence
• Execution time
• Rollback availability
3. Multi-LLM Consensus Viewer
Per action:
• GPT verdict
• DeepSeek verdict
• Risk engine result
• Final decision
• Confidence %
4. Repair Loop Monitor
• Error classification
• Repair attempt count
• Strategy switching history
5. Digital Twin Viewer
Before/after diff:
• Containers
• Ports
• Services
• Env vars
• Config files
6. Policy & Governance Panel
Adjust:
• Risk thresholds
• Consensus level (2/3 vs 3/3)
• Dry-run mode
• Strict security mode
No raw log reading should be required to understand state.
⸻
Digital Infrastructure Twin
Before and after execution:
• Docker graph snapshot
• Port map
• Service dependency map
• Process tree
• Environment diff
Unexpected changes → rollback trigger.
⸻
Chaos Testing & Self-Healing
System must survive:
• Broken docker network
• Port conflicts
• Killed containers
• Permission denied
• Corrupted configs
And auto-repair via strategy engine.
⸻
Technical Stack Requirements
• Advanced Ubuntu systems knowledge
• Docker / Compose mastery
• SSH automation
• Python orchestration
• LLM API orchestration
• WebSocket real-time dashboard
• DAG workflow modeling
• Security-first execution sandbox design
• DevOps + ML hybrid experience
Senior / Architect level only.
⸻
Deliverables
• Complete autonomous system
• One-shot installer
• Skill registry framework
• Strategy engine
• Multi-LLM consensus engine
• Dashboard
• Kestra integration
• Codex integration
• Xcode integration
• Chaos test suite
• Documentation
• Demo video
• Source repository
⸻
Timeline
8–12 weeks realistic
⸻
Budget- 800$