OpenHands Multi-Container Development Team Setup

OpenHands offers powerful capabilities for multi-agent development environments, though it requires careful architecture and security considerations to deploy effectively across development teams. This comprehensive analysis reveals both significant opportunities and critical challenges for implementing multi-container OpenHands setups.

High-Level Architecture Overview

graph TB
    subgraph "Development Team Infrastructure"
        subgraph "Host Docker Environment"
            DS[Docker Socket]
            DN[Docker Network]
        end
        
        subgraph "OpenHands Containers"
            OH1[OpenHands Main<br/>Container 1<br/>Frontend Dev]
            OH2[OpenHands Main<br/>Container 2<br/>Backend Dev]
            OH3[OpenHands Main<br/>Container 3<br/>Env Manager]
        end
        
        subgraph "Runtime Containers"
            RT1[Runtime Sandbox 1<br/>Frontend Tasks]
            RT2[Runtime Sandbox 2<br/>Backend Tasks]
            RT3[Runtime Sandbox 3<br/>Infrastructure]
        end
        
        subgraph "Application Containers"
            APP1[App Frontend<br/>Container]
            APP2[App Backend<br/>Container]
            APP3[Database<br/>Container]
        end
    end
    
    OH1 -->|Creates/Manages| RT1
    OH2 -->|Creates/Manages| RT2
    OH3 -->|Creates/Manages| RT3
    
    OH3 -->|Deploys| APP1
    OH3 -->|Deploys| APP2
    OH3 -->|Deploys| APP3
    
    DS -.->|Socket Mount| OH1
    DS -.->|Socket Mount| OH2
    DS -.->|Socket Mount| OH3
    
    DN -->|Network Isolation| RT1
    DN -->|Network Isolation| RT2
    DN -->|Network Isolation| RT3

Architecture fundamentals enable flexible multi-container deployments

OpenHands operates through a sophisticated client-server architecture with clear separation between the main application container, sandboxed runtime containers, and specialized agent delegation systems. The platform uses an event-driven architecture where AI agents process requests and execute actions in isolated Docker environments.

Component Communication Architecture

sequenceDiagram
    participant User
    participant OH as OpenHands Container
    participant RT as Runtime Container
    participant Docker as Docker Daemon
    participant Git as Git Repository
    
    User->>OH: Task Request
    OH->>Docker: Create Runtime Container
    Docker-->>OH: Container Created
    OH->>RT: Execute Task
    RT->>RT: Code Development
    RT->>Git: Create Branch
    RT->>Git: Commit Changes
    RT->>Git: Create PR
    RT-->>OH: Task Complete
    OH-->>User: PR Ready for Review

The container-based runtime system implements a three-component model: the main OpenHands application container manages orchestration and user interactions, dedicated runtime containers provide isolated execution environments, and communication occurs through RESTful APIs with WebSocket connections for real-time updates. OpenHands uses a three-tag image system (source-specific, lock, and generic tags) enabling efficient container reuse and rebuilding strategies.

While designed primarily for single-user deployments, OpenHands achieves multi-container capabilities through third-party integration solutions. Daytona provides agent-agnostic infrastructure middleware with multiple parallel sandbox environments and resource optimization. E2B Runtime offers cloud-based secure sandbox environments with Python and JavaScript SDK support. These partnerships enable enterprise-scale multi-container deployments beyond the basic single-user architecture.

Role-based container configuration supports specialized development teams

OpenHands supports differentiated agent roles through its microagent architecture and plugin system. Development teams can deploy specialized containers for distinct functions:

Multi-Agent Team Structure

graph LR
    subgraph "Scrum Master Agent"
        SM[Environment Manager<br/>Container]
        SMT[Tasks:<br/>- Orchestration<br/>- Deploy Management<br/>- Resource Allocation]
    end
    
    subgraph "Development Agents"
        FE[Frontend Dev<br/>Container]
        FET[Tasks:<br/>- React/Vue Dev<br/>- UI Testing<br/>- CSS/Design]
        
        BE[Backend Dev<br/>Container]
        BET[Tasks:<br/>- API Development<br/>- Database Logic<br/>- Integration]
        
        QA[QA/Testing<br/>Container]
        QAT[Tasks:<br/>- Test Writing<br/>- E2E Testing<br/>- Code Review]
    end
    
    SM -->|Assigns Tasks| FE
    SM -->|Assigns Tasks| BE
    SM -->|Assigns Tasks| QA
    
    FE -->|PR| SM
    BE -->|PR| SM
    QA -->|Review| SM

Frontend development agents can be configured with web-focused capabilities using the BrowsingAgent for web interface testing and CodeActAgent for JavaScript/TypeScript development. Backend development agents utilize specialized runtime environments with database plugins and API development tools. Environment managers with elevated privileges operate through DelegatorAgent configurations that orchestrate task distribution and coordinate between other specialized agents.

The microagent system enables repository-specific customization through .openhands/microagents/ directories containing Markdown files with YAML frontmatter. These microagents provide domain-specific knowledge and can be triggered by keywords or remain always active. Custom plugins initialize when runtime clients start, associated with Agent.sandbox_plugins configuration, allowing teams to create specialized environments for different development roles.

Container specialization occurs through runtime configuration rather than different base images. Teams can deploy multiple containers with identical OpenHands installations but different environment variables, plugin configurations, and resource allocations to create frontend-focused, backend-focused, and infrastructure management roles.

Docker integration presents security trade-offs requiring careful implementation

OpenHands currently relies on Docker socket mounting as its primary integration strategy, requiring /var/run/docker.sock access to create and manage sandbox containers. This approach provides excellent performance and resource efficiency but introduces critical security vulnerabilities - mounting the Docker socket grants root-equivalent access to the host system.

Security Architecture Options

graph TB
    subgraph "Option 1: Socket Mount (Current)"
        H1[Host System]
        D1[Docker Daemon]
        S1[/var/run/docker.sock]
        OH1[OpenHands Container]
        RT1[Runtime Container]
        
        H1 --> D1
        D1 --> S1
        S1 -.->|RISK: Root Access| OH1
        OH1 -->|Creates| RT1
    end
    
    subgraph "Option 2: Docker-in-Docker"
        H2[Host System]
        D2[Host Docker]
        DIND[DinD Container<br/>Nested Docker]
        OH2[OpenHands Container]
        RT2[Runtime Container]
        
        H2 --> D2
        D2 --> DIND
        DIND -->|Isolated| OH2
        OH2 -->|Creates| RT2
    end
    
    subgraph "Option 3: API Proxy"
        H3[Host System]
        D3[Docker Daemon]
        PROXY[Docker API Proxy<br/>Permission Filter]
        OH3[OpenHands Container]
        RT3[Runtime Container]
        
        H3 --> D3
        D3 --> PROXY
        PROXY -->|Filtered Access| OH3
        OH3 -->|Creates| RT3
    end

Docker-in-Docker (DinD) offers enhanced security through nested Docker daemon isolation, preventing OpenHands containers from directly accessing the host Docker daemon. However, DinD introduces 10-15% performance overhead and requires privileged containers with additional resource allocation. The implementation requires separate DinD containers linked to OpenHands instances.

Recommended security hardening strategies include implementing user namespace remapping to map container root to unprivileged host users, deploying SELinux or AppArmor mandatory access controls, creating isolated Docker networks for container communication, and establishing comprehensive audit trails for all Docker API calls. Production environments should avoid socket mounting entirely, implementing rootless Docker or DinD configurations with proper resource limits.

Network isolation strategies involve creating custom bridge networks, implementing container-to-container communication through DNS resolution, and restricting network binding to localhost (127.0.0.1) rather than all interfaces. Teams should implement dedicated networks for different development environments to prevent cross-contamination between projects.

Conflict prevention requires orchestrated coordination between autonomous developers

Multiple OpenHands agents working on the same codebase necessitate sophisticated coordination mechanisms. The platform’s single-user design means teams must implement external orchestration layers to prevent conflicts.

GitFlow Coordination Pattern

gitGraph
    commit id: "main"
    branch develop
    checkout develop
    commit id: "dev-base"
    
    branch openhands/agent-fe/feature-ui
    checkout openhands/agent-fe/feature-ui
    commit id: "UI components"
    commit id: "Style updates"
    
    checkout develop
    branch openhands/agent-be/feature-api
    checkout openhands/agent-be/feature-api
    commit id: "API endpoints"
    commit id: "Database schema"
    
    checkout develop
    branch openhands/agent-qa/tests
    checkout openhands/agent-qa/tests
    commit id: "Unit tests"
    commit id: "Integration tests"
    
    checkout develop
    merge openhands/agent-fe/feature-ui
    merge openhands/agent-be/feature-api
    merge openhands/agent-qa/tests
    
    checkout main
    merge develop tag: "v1.0"

Agent Coordination State Machine

stateDiagram-v2
    [*] --> Idle
    Idle --> ClaimingTask: Task Available
    ClaimingTask --> CheckingConflicts: Task Claimed
    CheckingConflicts --> WorkingOnTask: No Conflicts
    CheckingConflicts --> WaitingForAccess: Conflicts Detected
    WaitingForAccess --> WorkingOnTask: Access Granted
    WorkingOnTask --> CommittingCode: Work Complete
    CommittingCode --> CreatingPR: Code Committed
    CreatingPR --> Idle: PR Created
    WorkingOnTask --> ResolvingConflict: Merge Conflict
    ResolvingConflict --> CommittingCode: Conflict Resolved

Effective coordination patterns include deploying an orchestrator agent that manages overall GitFlow coordination and branch strategy, while worker agents handle specific development tasks within assigned branches. An integration agent specializes in merge conflict detection and resolution, creating a hierarchical structure that prevents simultaneous file modifications.

Proactive conflict prevention strategies involve implementing file-level coordination through code ownership models, breaking complex features into non-overlapping subtasks assigned to different agents, and establishing agent-to-agent communication protocols for coordinating file access. Temporal coordination through sequential task processing, lightweight file locking, and time-windowed operations minimizes overlap between agents.

Advanced conflict detection integrates with tools like CodeRabbit, Bito AI, and PR-Agent for proactive conflict identification. These systems detect not just line-based conflicts but logical inconsistencies and dependency relationships that could cause integration issues.

GitFlow implementation enables automated branch management and pull requests

OpenHands agents can effectively implement GitFlow patterns through automated branch naming conventions using the openhands/ prefix, creating clear identification of AI-generated branches. Agent-specific workflows involve each agent creating dedicated feature branches (openhands/agent-{id}/feature-{task}) while working against shared development branches with automated merging.

Pull request automation capabilities include direct GitHub/GitLab API integration for branch management, atomic commits with AI-generated commit messages following conventional commit standards, and automated PR creation with comprehensive descriptions. Integration with PR-Agent and similar tools enables automated code review with line-by-line suggestions and security vulnerability detection.

Automated merge strategies implement intelligent three-way merging that considers the intent of each agent’s changes. Context-aware merging uses LLM capabilities to understand code semantics during conflict resolution, while conflict scoring prioritizes issues based on impact and complexity for human review. Resolution hierarchies escalate from automated resolution through agent negotiation to human intervention for critical conflicts.

Repository access control implements graduated permission levels: read-only agents for analysis, developer agents with branch creation rights, and maintainer agents with merge capabilities. Token-based authentication uses GitHub/GitLab personal access tokens with scoped permissions, maintaining clear agent identity management and audit trails.

Infrastructure as Code generation supports comprehensive deployment automation

OpenHands demonstrates strong capabilities for generating infrastructure configurations across multiple platforms. The platform can create Terraform HCL files, AWS CloudFormation templates in JSON and YAML formats, and comprehensive Kubernetes manifests including deployments, services, and Helm charts.

IaC Generation and Deployment Flow

flowchart LR
    subgraph "OpenHands Environment Manager"
        OH[OpenHands<br/>Env Manager]
        GEN[IaC Generator]
        
        OH --> GEN
    end
    
    subgraph "Generated Configurations"
        TF[Terraform Files]
        K8S[K8s Manifests]
        CF[CloudFormation]
        GH[GitHub Actions]
        
        GEN --> TF
        GEN --> K8S
        GEN --> CF
        GEN --> GH
    end
    
    subgraph "Git Repository"
        REPO[Infrastructure<br/>Repository]
        PR[Pull Request]
        
        TF --> PR
        K8S --> PR
        CF --> PR
        GH --> PR
        PR --> REPO
    end
    
    subgraph "CI/CD Pipeline"
        GA[GitHub Actions]
        PLAN[Terraform Plan]
        APPLY[Terraform Apply]
        
        REPO --> GA
        GA --> PLAN
        PLAN -->|Approved| APPLY
    end
    
    subgraph "Cloud Environments"
        AWS[AWS EKS]
        HRK[Heroku]
        
        APPLY --> AWS
        APPLY --> HRK
    end

Multi-Environment Deployment Architecture

graph TB
    subgraph "OpenHands Infrastructure Management"
        ENV[Environment Manager Agent]
        IaC[IaC Generation Engine]
    end
    
    subgraph "Development"
        DEV_K8S[Dev Kubernetes Cluster]
        DEV_APP[Dev Applications]
        DEV_DB[Dev Database]
    end
    
    subgraph "Staging"
        STG_K8S[Staging EKS Cluster]
        STG_APP[Staging Applications]
        STG_DB[Staging Database]
    end
    
    subgraph "Production"
        PRD_K8S[Production EKS Cluster]
        PRD_APP[Production Applications<br/>Blue/Green]
        PRD_DB[Production Database<br/>Multi-AZ]
    end
    
    ENV --> IaC
    IaC -->|Local Docker Compose| DEV_K8S
    IaC -->|Terraform + K8s| STG_K8S
    IaC -->|Terraform + K8s + GitOps| PRD_K8S
    
    DEV_K8S --> DEV_APP
    DEV_APP --> DEV_DB
    
    STG_K8S --> STG_APP
    STG_APP --> STG_DB
    
    PRD_K8S --> PRD_APP
    PRD_APP --> PRD_DB

AWS EKS integration occurs through generated infrastructure configurations rather than direct API access. OpenHands can create EKS cluster definitions using Terraform or CloudFormation, generate node group configurations with autoscaling policies, configure networking components including VPC and security groups, and set up IAM roles and policies for cluster access. The platform generates Kubernetes manifests for application deployment and implements GitOps workflows with tools like Argo CD.

Heroku deployment automation includes generating Procfiles for process definition, heroku.yml files for container-based deployments, configuring buildpacks and dependencies, and creating GitHub Actions workflows for automated deployment. OpenHands supports Git-based deployment strategies, Docker container deployment, database configuration, and environment promotion workflows.

Infrastructure provisioning workflows follow a configuration-first approach: generating infrastructure configurations, creating CI/CD pipeline definitions, setting up deployment automation scripts, and configuring environment promotion and rollback strategies. This approach maintains separation between configuration generation and deployment execution, leveraging existing CI/CD infrastructure and security policies.

CI/CD pipeline integration creates deployment configs for GitHub Actions execution

OpenHands provides robust GitHub Actions integration with built-in GitHub Actions for automatic issue resolution and comprehensive workflow generation capabilities. The platform can create complex CI/CD workflows with multiple jobs, dependencies, and conditional execution based on branch patterns and file changes.

Integration patterns involve OpenHands generating infrastructure configurations and deployment workflows while actual deployment execution occurs through established CI/CD systems. This approach maintains audit trails, approval processes, and GitOps workflows while leveraging AI-generated configurations.

Example integration workflows include Terraform planning on pull requests, automated deployment on main branch merges, environment-specific deployment strategies, and rollback mechanisms for failed deployments. OpenHands can generate environment promotion workflows, blue-green deployment strategies, and comprehensive deployment documentation.

CI/CD best practices implemented by OpenHands include incremental infrastructure changes, integration with Git workflows for versioning, automatic documentation generation, and incorporation of security best practices in generated configurations. The platform supports multi-environment configurations with environment-specific variables and promotion workflows.

Local container orchestration requires careful resource management and networking

Effective local orchestration involves Docker Compose configurations for multi-container development environments. Teams should implement custom bridge networks for container isolation, named volumes for persistent data, and appropriate resource limits for CPU and memory constraints.

Network Architecture for Multi-Agent Setup

graph TB
    subgraph "Host Network Stack"
        subgraph "Frontend Network 172.20.0.0/24"
            FE_OH[Frontend OpenHands<br/>172.20.0.10]
            FE_RT[Frontend Runtime<br/>172.20.0.11]
            FE_APP[Frontend App<br/>172.20.0.12]
        end
        
        subgraph "Backend Network 172.21.0.0/24"
            BE_OH[Backend OpenHands<br/>172.21.0.10]
            BE_RT[Backend Runtime<br/>172.21.0.11]
            BE_APP[Backend App<br/>172.21.0.12]
            DB[Database<br/>172.21.0.20]
        end
        
        subgraph "Management Network 172.22.0.0/24"
            MGR_OH[Manager OpenHands<br/>172.22.0.10]
            MGR_RT[Manager Runtime<br/>172.22.0.11]
            MONITOR[Monitoring Stack<br/>172.22.0.20]
        end
        
        ROUTER[Network Router]
    end
    
    FE_OH -.-> ROUTER
    BE_OH -.-> ROUTER
    MGR_OH -.-> ROUTER
    
    ROUTER --> FE_OH
    ROUTER --> BE_OH
    
    FE_RT --> FE_APP
    BE_RT --> BE_APP
    BE_RT --> DB
    
    MGR_OH --> MONITOR
    MONITOR -.->|Metrics| FE_OH
    MONITOR -.->|Metrics| BE_OH

Resource Allocation Strategy

pie title "Container Resource Distribution (32GB Total)"
    "Frontend OpenHands" : 4
    "Frontend Runtime" : 2
    "Backend OpenHands" : 4
    "Backend Runtime" : 2
    "Manager OpenHands" : 6
    "Manager Runtime" : 4
    "Application Containers" : 6
    "Monitoring/Logging" : 2
    "System Reserved" : 2

Container lifecycle management requires automated cleanup strategies for stopped containers, unused images, and networks. Health checks and monitoring ensure container availability and performance. Development workflow optimization involves custom networks for different environments, volume management for code synchronization, and resource allocation based on development team size.

Network architecture should implement container-to-container communication through DNS resolution, service discovery mechanisms, and traffic isolation through separate networks for frontend, backend, and database components. Load balancing strategies distribute requests across multiple agent containers with session affinity for ongoing work.

Scaling considerations include horizontal scaling through container replication, auto-scaling triggers based on CPU and memory utilization, and high availability through multi-zone deployment with shared storage for workspace persistence.

Security considerations demand comprehensive access control and monitoring

Critical security risks center on Docker socket access granting root-equivalent host access. Mitigation strategies include implementing Docker API proxies with permission filtering, using dedicated Docker-in-Docker setups, and restricting network binding to localhost only in production environments.

Security Monitoring Architecture

flowchart TB
    subgraph "Security Layers"
        subgraph "Authentication"
            SSO[Enterprise SSO<br/>SAML/OIDC]
            MFA[Multi-Factor Auth]
            RBAC[Role-Based Access]
        end
        
        subgraph "Network Security"
            FW[Firewall Rules]
            NSG[Network Policies]
            TLS[TLS Encryption]
        end
        
        subgraph "Container Security"
            SCAN[Image Scanning]
            POL[Pod Security Policies]
            ADM[Admission Controllers]
        end
        
        subgraph "Monitoring"
            SIEM[SIEM Integration]
            AUDIT[Audit Logs]
            ALERT[Security Alerts]
        end
    end
    
    subgraph "OpenHands Infrastructure"
        OH[OpenHands Containers]
        RT[Runtime Containers]
        API[API Gateway]
    end
    
    SSO --> MFA
    MFA --> RBAC
    RBAC --> API
    
    API --> FW
    FW --> NSG
    NSG --> OH
    
    OH --> TLS
    TLS --> RT
    
    SCAN --> OH
    POL --> RT
    ADM --> OH
    
    OH --> AUDIT
    RT --> AUDIT
    AUDIT --> SIEM
    SIEM --> ALERT

Access Control Matrix

graph TB
    subgraph "User Roles"
        ADMIN[Admin Users<br/>Full Control]
        DEV[Developer Users<br/>Code + Deploy Dev]
        REVIEW[Reviewer Users<br/>Read + Comment]
        VIEWER[Viewer Users<br/>Read Only]
    end
    
    subgraph "Agent Permissions"
        ENV_MGR[Environment Manager<br/>- Create Infrastructure<br/>- Deploy All Envs<br/>- Manage Secrets]
        
        SR_DEV[Senior Dev Agent<br/>- Create PRs<br/>- Merge to Develop<br/>- Deploy to Dev]
        
        JR_DEV[Junior Dev Agent<br/>- Create PRs<br/>- Work in Feature Branches<br/>- Read Access]
        
        QA_AGENT[QA Agent<br/>- Create Test PRs<br/>- Run Tests<br/>- Comment on PRs]
    end
    
    subgraph "Resources"
        PROD[Production<br/>Environment]
        STG[Staging<br/>Environment]
        DEV_ENV[Development<br/>Environment]
        REPO[Git Repository]
    end
    
    ADMIN --> ENV_MGR
    DEV --> SR_DEV
    DEV --> JR_DEV
    REVIEW --> QA_AGENT
    
    ENV_MGR --> PROD
    ENV_MGR --> STG
    ENV_MGR --> DEV_ENV
    
    SR_DEV --> STG
    SR_DEV --> DEV_ENV
    SR_DEV --> REPO
    
    JR_DEV --> DEV_ENV
    JR_DEV -.->|Feature Branches| REPO
    
    QA_AGENT --> DEV_ENV
    QA_AGENT -.->|Comments| REPO

Access control frameworks require OAuth integration for enterprise SSO, API key-based access for programmatic usage, and role-based access control (RBAC) with developer, reviewer, and admin permission levels. Agent privilege isolation involves separate container namespaces per user/project, resource quotas per agent, and network segmentation for agent communications.

Monitoring and logging strategies implement container-level monitoring with Prometheus and Grafana, log aggregation through FluentD or Fluent Bit, and comprehensive agent activity tracking. Key metrics include container resource utilization, agent task completion rates, network traffic patterns, and Docker API call frequency monitoring.

Production security hardening involves enterprise-grade authentication systems, static IP configurations, Cloud Operations Suite integration, and regular security audits. Teams should implement disaster recovery procedures, cross-region replication for critical deployments, and comprehensive backup strategies for workspace data and agent states.

Enterprise deployments demonstrate scalable multi-agent patterns

Shakudo Integration provides enterprise-grade OpenHands deployment with VPC and on-premise infrastructure options, enterprise security and compliance controls, and deployment time reduction from months to weeks. Daytona Platform offers agent-agnostic middleware infrastructure with isolated secure environments, multi-agent parallel execution support, and dynamic workspace management.

Enterprise Deployment Architecture

graph TB
    subgraph "Enterprise Infrastructure"
        subgraph "Load Balancer Layer"
            LB[Application Load Balancer]
            WAF[Web Application Firewall]
        end
        
        subgraph "Kubernetes Cluster (EKS)"
            subgraph "OpenHands Namespace"
                OH_POD1[OpenHands Pod 1<br/>Replicas: 3]
                OH_POD2[OpenHands Pod 2<br/>Replicas: 3]
                OH_POD3[OpenHands Pod 3<br/>Replicas: 3]
            end
            
            subgraph "Runtime Namespace"
                RT_POOL[Runtime Pod Pool<br/>Auto-scaling: 1-50]
            end
            
            subgraph "Monitoring Namespace"
                PROM[Prometheus]
                GRAF[Grafana]
                ALERT[AlertManager]
            end
        end
        
        subgraph "Data Layer"
            RDS[RDS PostgreSQL<br/>Multi-AZ]
            REDIS[ElastiCache Redis<br/>Cluster Mode]
            S3[S3 Buckets<br/>Workspace Storage]
        end
        
        subgraph "Security Layer"
            IAM[IAM Roles]
            SG[Security Groups]
            KMS[KMS Encryption]
        end
    end
    
    LB --> WAF
    WAF --> OH_POD1
    WAF --> OH_POD2
    WAF --> OH_POD3
    
    OH_POD1 --> RT_POOL
    OH_POD2 --> RT_POOL
    OH_POD3 --> RT_POOL
    
    OH_POD1 --> RDS
    OH_POD1 --> REDIS
    OH_POD1 --> S3
    
    PROM --> OH_POD1
    PROM --> RT_POOL
    GRAF --> PROM
    ALERT --> PROM
    
    IAM -.-> OH_POD1
    SG -.-> RT_POOL
    KMS -.-> S3

Scaling Patterns

graph LR
    subgraph "Small Team (3-5 devs)"
        ST_OH[1 OpenHands<br/>Container]
        ST_RT[3-5 Runtime<br/>Containers]
        ST_APP[Single App<br/>Instance]
    end
    
    subgraph "Medium Team (10-20 devs)"
        MT_OH[3 OpenHands<br/>Containers]
        MT_RT[10-20 Runtime<br/>Containers]
        MT_APP[App Cluster<br/>3 Instances]
        MT_LB[Load Balancer]
    end
    
    subgraph "Enterprise (50+ devs)"
        ET_K8S[Kubernetes<br/>Auto-scaling]
        ET_OH[OpenHands<br/>Deployment<br/>HPA: 3-10]
        ET_RT[Runtime<br/>Deployment<br/>HPA: 10-100]
        ET_APP[App Deployment<br/>Multi-Region]
    end
    
    ST_OH --> ST_RT
    ST_RT --> ST_APP
    
    MT_LB --> MT_OH
    MT_OH --> MT_RT
    MT_RT --> MT_APP
    
    ET_K8S --> ET_OH
    ET_OH --> ET_RT
    ET_RT --> ET_APP

Google Cloud production deployments utilize Cloud Run, Secret Manager, and Load Balancer components with static IP configuration and custom domain integration. However, serverless limitations create challenges for Docker socket access, requiring alternative architecture patterns.

Multi-agent system architectures include hierarchical master-subordinate models with coordinator agents and specialized agents for different tasks, and decentralized peer-to-peer networks with fault-tolerant design and dynamic agent discovery. These patterns enable enterprise-scale deployments with proper resource allocation and coordination mechanisms.

Infrastructure requirements include minimum 4 vCPU and 8GB RAM per agent, with recommendations for 8 vCPU and 16GB RAM for complex tasks. Storage requirements involve 50GB+ per workspace with fast I/O capabilities. Scaling strategies implement Kubernetes deployments with horizontal pod autoscaling based on CPU, memory, and queue depth metrics.

Conclusion

OpenHands multi-container setups offer powerful capabilities for development teams willing to invest in proper architecture, security, and coordination mechanisms. Success requires starting with simple single-agent deployments, implementing robust security controls around Docker access, establishing clear agent coordination protocols, and leveraging enterprise integration patterns through platforms like Daytona and Shakudo.

Critical success factors include treating Docker socket access as a significant security risk requiring mitigation, implementing comprehensive monitoring and logging for all agent activities, establishing clear role boundaries and task coordination between agents, and maintaining human oversight for critical operations. Teams should gradually scale from basic deployments to complex multi-agent architectures, ensuring proper security hardening and operational procedures at each stage.

The platform’s strength lies in its sophisticated event-driven architecture, comprehensive Docker integration capabilities, and flexible agent coordination systems. However, teams must carefully balance the automation benefits against the security risks and operational complexity of multi-container autonomous development environments.