Implement Multi-Agent Agentic Infrastructure Control Platform

[Copilot]

Overview

Implements a governed automation platform for Network, Edge, and Cloud infrastructure using a multi-agent architecture. Provides policy enforcement, blast radius prediction, and multi-region failover capabilities.

Architecture

Multi-Agent System

• Policy Agent: OPA integration, blast radius prediction (4 severity levels), 3-tier approval workflow (Auto/Require/Deny)
• Intent Agent: NLP-based intent parsing, AI script generation with template fallback, dangerous command detection
• Deployment Agent: Kubernetes orchestration, multi-region deployment, automated failover
• Compliance Agent: Multi-framework support (SOC2/HIPAA/PCI-DSS/GDPR), audit logging, compliance markers
• Inference Agent: AI-driven recommendations, outcome prediction, historical analysis

Core Framework

• Base agent class with async message passing via orchestrator
• Message bus for pub/sub communication patterns
• Policy enforcement engine with local fallback when OPA unavailable
• Blast radius predictor analyzing impact across services, regions, users, data sensitivity

Adapters & Integration

• Cloud providers: AWS, Azure, GCP
• Edge infrastructure management
• OPA policy engine (Rego format)
• gRPC API layer
• Kubernetes client

Key Technical Decisions

Policy Enforcement: Impact score calculation (0-1) using weighted factors:

impact_weights = {
    "affected_services": 0.3,
    "affected_regions": 0.25,
    "affected_users": 0.25,
    "data_impact": 0.2
}

Blast Radius Levels: Maps impact scores to approval decisions:

• Low (< 0.25): Auto-approve
• Medium (0.25-0.5): Require approval
• High (0.5-0.75): Require approval
• Critical (≥ 0.75): Deny

Failover: Health check based with configurable intervals, automatic region switching when primary unhealthy.

API Endpoints

FastAPI implementation with 15+ endpoints covering:

• Intent processing: /api/v1/intent/process
• Deployment ops: /api/v1/deployment/{deploy,scale,rollback}
• Policy checks: /api/v1/policy/check
• Compliance: /api/v1/compliance/check
• Failover: /api/v1/failover/{configure,trigger}
• Audit logs: /api/v1/audit/logs

Security

All dependencies updated to patched versions:

• aiohttp: 3.9.1 → 3.13.3 (zip bomb, DoS, directory traversal)
• fastapi: 0.108.0 → 0.109.1 (ReDoS)
• protobuf: 4.25.1 → 5.29.6 (JSON recursion bypass, DoS)
• torch: 2.1.2 → 2.6.0 (buffer overflow, use-after-free, RCE)
• transformers: 4.36.2 → 4.48.0 (deserialization)

Files

• 48+ files, ~4,930 LOC
• 37 Python modules across 5 agents + adapters
• 3 OPA policy templates (deployment, script validation, compliance)
• Validation script with 5 integration tests

Original prompt

Overview

Implement a comprehensive Multi-Agent Agentic Infrastructure Control Platform - a governed automation solution for Networks, Edge, and Cloud infrastructure.

Visual Reference

Core Platform Features to Implement

1. Multi-Agent Architecture

Create a multi-agent system with the following specialized agents:

• Policy Agent - Enforces governance policies across all automation
• Intent Agent - Processes user intents and translates to actionable scripts
• CD Agent (Continuous Deployment) - Handles deployment orchestration
• Inference Agent - AI-driven decision making and script generation
• Compliance Agent - Monitors and ensures compliance markers

2. Key Platform Components

Policy Enforcement System

• Policy enforcement engine using OPA (Open Policy Agent)
• Policy validation and governance checks
• Blast radius prediction before execution

Blast Radius Prediction

• Analyze potential impact of automation changes
• Multi-radius prediction capabilities
• Approval workflow for high-risk operations (Auto Approval → Require Approval → Deny)

Multi-Region Failover

• Global routing capabilities
• Multi-region infrastructure support
• Failover automation between regions

Script Library & Adapters

• OPA Policy Engine integration
• gRPC API Layer for communication
• Kubernetes Deployment adapters
• Rosetta Environment adapters
• Extensible adapter framework

3. Customer Journey Flow

Implement the workflow:

1. Intent Input → User provides automation intent
2. AI Script Injections → AI generates/modifies scripts
3. Policy Simulations → Validate against policies
4. Additions Policy → Apply additional governance
5. Execution → Governed execution

4. Cost Optimization Features

• Bandwidth savings monitoring
• Compute efficiency tracking
• Storage rebalancing automation

5. Partner Ecosystem Integration

• Vendor integrations
• MSP (Managed Service Provider) support
• Cloud provider adapters (multi-cloud)

Technical Requirements

Agent Framework Structure

/agents
├── core/
│   ├── base_agent.py          # Base agent class
│   ├── agent_orchestrator.py  # Multi-agent coordination
│   └── message_bus.py         # Inter-agent communication
├── policy_agent/
│   ├── policy_engine.py       # OPA integration
│   ├── blast_radius.py        # Impact prediction
│   └── enforcement.py         # Policy enforcement
├── intent_agent/
│   ├── intent_parser.py       # NLP intent parsing
│   ├── script_generator.py    # AI script generation
│   └── validation.py          # Intent validation
├── deployment_agent/
│   ├── kubernetes.py          # K8s deployment
│   ├── multi_region.py        # Multi-region support
│   └── failover.py            # Failover automation
├── compliance_agent/
│   ├── compliance_checker.py  # Compliance validation
│   ├── audit_logger.py        # Audit trail
│   └── markers.py             # Compliance markers
├── adapters/
│   ├── grpc_layer.py          # gRPC API
│   ├── opa_adapter.py         # OPA integration
│   ├── cloud_providers/       # AWS, Azure, GCP adapters
│   └── edge_adapters/         # Edge infrastructure
├── config/
│   ├── policies/              # Policy definitions
│   └── settings.py            # Platform configuration
└── api/
    ├── routes.py              # API endpoints
    └── schemas.py             # API schemas

Key Technologies

• Python with async support for agents
• gRPC for inter-agent communication
• OPA (Open Policy Agent) for policy enforcement
• Kubernetes client for deployments
• OpenAI/LLM integration for AI-driven automation

Competitive Differentiators to Implement

Based on the competitive landscape shown:

• ✅ Policy Enforcement (vs legacy automation tools)
• ✅ Bolity Radius prediction
• ✅ Offline Support capability
• ✅ Multi-Tenant architecture
• ✅ Marketplace for scripts/adapters

Deliverables

1. Complete multi-agent framework with all core agents
2. Policy enforcement engine with OPA integration
3. Blast radius prediction system
4. Multi-region failover support
5. gRPC API layer
6. Kubernetes deployment adapters
7. Configuration and policy templates
8. API endpoints for platform interaction
9. Documentation and README
10. Example usage and demo scripts

Market Context

• TAM: $90B+
• SAM: $8B
• Initial Target: $750M
• Focus: Governed Automation for the Future

This pull request was created from Copilot chat.

---

✨ Let Copilot coding agent set things up for you — coding agent works faster and does higher quality work when set up for your repo.