Samsara AI - Enterprise Multi-Agent Framework

Project Vision

Samsara AI redefines enterprise automation through a quantum-ready, zero-trust multi-agent framework that combines:

• Military-Grade Security (FIPS 140-3 validated crypto, Zero Trust Architecture)
• Autonomous Collaboration (DAG-based workflows with self-healing capabilities)
• Regulatory Compliance (GDPR/HIPAA/PCI-DSS built-in controls)

Key Enterprise Features

Feature	Technology Stack	Compliance
Agent Swarm Orchestration	Kubernetes CRD Operators, Istio Service Mesh	SOC2 Type 2
Federated Learning	PyTorch Federated, Homomorphic Encryption	NIST SP 800-208
Observability	OpenTelemetry, Prometheus, Grafana Loki	ISO 27001
Post-Quantum Security	Kyber-1024, Dilithium ML-DSA	FIPS 203 (Draft)
Cost Governance	ML-driven Resource Optimizer, FinOps API	AWS CCM

Architecture Overview

┌──────────────────────────────┐
│   Samsara Control Plane       │
│  ┌─────────────┐ ┌─────────┐ │
│  │ API Gateway │ │ Policy  │ │      Edge
│  │ (FastAPI)   │ │ Engine  │◄├──┐  Deployment
│  └──────┬──────┘ └────┬────┘ │  │  (IoT/5G)
│         ▼              ▼      │  └─────────┐
│  ┌─────────────┐ ┌─────────┐ │             ▼
│  │ Agent       │ │ Key     │ │  ┌─────────────────────┐
│  │ Lifecycle   │ │ Mgmt    │ │  │ Samsara Agent Swarm │
│  │ Manager     │ │ (Vault) │ │  │ ┌───┐ ┌───┐ ┌───┐   │
│  └──────┬──────┘ └────┬────┘ │  │ │Agt│ │Agt│ │Agt│   │
│         ▼              ▼      │  │ └─┬─┘ └─┬─┘ └─┬─┘   │
│  ┌──────────────────────────┐│  │   ▼     ▼     ▼      │
│  │ Distributed Workflow Engine │  │  Auto-Scaling Group  │
│  │ (Apache Airflow + Dask)   ││  └─────────────────────┘
└──────────────────────────────┘

Core Architecture

%%{init: {'theme': 'dark'}}%%
flowchart TD
    subgraph ControlPlane
        APIGateway([API Gateway]) --> AuthZ[[RBAC Engine]]
        AuthZ --> Orchestrator{{Orchestrator}}
        Orchestrator -->|Scaling Policies| AutoScaler[Auto-Scaler]
    end

    subgraph DataPlane
        Agents[Agent Swarm] -->|Secure GRPC| FederatedLearning[Federated Learning]
        Agents -->|Encrypted| SharedMemory[(Shared Memory)]
        FederatedLearning --> Aggregator{{Secure Aggregator}}
    end

    ControlPlane -->|mTLS| DataPlane
    APIGateway -->|OAuth2| Observability[[Prometheus, Loki]]

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Multi-Agent Collaboration

%%{init: {'theme': 'dark'}}%%
sequenceDiagram
    participant User as Client
    participant API as API Gateway
    participant Orchestrator
    participant Agent1 as Data Agent
    participant Agent2 as Model Agent
    participant Agent3 as Auditor Agent

    User->>API: POST /task {"type": "risk-analysis"}
    API->>Orchestrator: AuthZ Check
    Orchestrator->>Agent1: Spawn (Bloomberg API)
    Orchestrator->>Agent2: Spawn (PyTorch Model)
    Orchestrator->>Agent3: Spawn (Compliance Auditor)
    
    Agent1->>SharedDB: Write Encrypted Data
    Agent2->>SharedDB: Read Data → Run Simulation
    Agent3->>Agent2: Validate Outputs
    Agent2->>Aggregator: Submit Results (+ DP Noise)
    Aggregator->>API: Final Report
    API->>User: 201 Created + Report URL

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Zero-Trust Security

%%{init: {'theme': 'dark'}}%%
flowchart LR
    subgraph Identity
        TPM[[TPM 2.0]] -->|Attest| Vault[HashiCorp Vault]
        Vault -->|Dynamic Secrets| API
    end

    subgraph DataFlow
        Agent -->|Kyber-1024| Enclave[Confidential Compute]
        Enclave -->|Homomorphic| Processing[FHE Operations]
    end

    subgraph Policy
        OPA[[Open Policy Agent]] -->|Realtime Checks| Audit[Audit Logs]
        Audit --> Splunk{{Splunk CIM}}
    end

    Identity --> DataFlow
    Policy --> DataFlow

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Getting Started

Prerequisites

• Kubernetes 1.27+ (with cert-manager)
• PostgreSQL 15+ (with pg_partman)
• HashiCorp Vault 1.15+

Installation

# Clone with submodules
git clone --recurse-submodules https://github.com/samsara-ai/core.git

# Initialize infrastructure
terraform -chdir=infrastructure/aws-eks init
terraform apply -var="cluster_name=samsara-prod"

# Deploy Samsara AI
helm install samsara ./charts/samsara \
  --set global.encryptionKey="$(vault read -field=key samsara/encryption)" \
  --set prometheus.retention=1y

Usage Example: Financial Risk Analysis

from samsara import agents, workflows

# Create agent swarm
risk_agents = agents.Swarm(
    template="financial-risk:v4.2",
    count=50,
    config={
        "market_data_source": "bloomberg",
        "compliance_profile": "basel-iii"
    }
)

# Define ML workflow
with workflows.DAG("risk-simulation") as dag:
    data_task = dag.add_task(agents.DataCollector(regions=["NA", "EU"]))
    model_task = dag.add_task(agents.RiskModelRunner(ensemble_size=1000))
    audit_task = dag.add_task(agents.ComplianceAuditor())
    
    data_task >> model_task >> audit_task

# Execute with zero-trust verification
results = dag.execute(verification_mode="cryptographic")

Deployment Options

Environment	Configuration	SLA
Kubernetes	docs/deployment/k8s.md	99.95%
AWS Outposts	docs/deployment/aws.md	99.9%
Hybrid Edge	docs/deployment/edge.md	99.5%
Docker Compose	docs/deployment/docker.md	Dev Only

Contribution Guidelines

1. Security First: All PRs must pass:

• Static Application Security Testing (Semgrep)
• Software Bill of Materials (SBOM) audit

2. Compliance Checks:

make compliance-check RELEASE_TAG=v2.1.0

3. Certified Builds:

• SLSA Level 3 provenance required for production artifacts
• Signed with Cosign and Sigstore