Cloud Workflow

Design and orchestrate event-driven automation workflows with intelligent agent coordination and message queue processing.

Quick Start

// Create a CI/CD workflow const workflow = await mcp__flow-nexus__workflow_create({ name: "CI/CD Pipeline", description: "Automated testing and deployment", steps: [ { id: "test", action: "run_tests", agent: "tester" }, { id: "build", action: "build_app", agent: "builder" }, { id: "deploy", action: "deploy_prod", agent: "deployer" } ], triggers: ["push_to_main", "manual_trigger"] });

// Execute the workflow await mcp__flow-nexus__workflow_execute({ workflow_id: workflow.workflow_id, input_data: { branch: "main" }, async: true });

When to Use

• Automating CI/CD pipelines with multiple stages

• Orchestrating data processing and ETL workflows

• Creating event-driven automation for business processes

• Managing multi-stage review and approval workflows

• Scheduling recurring automated tasks

• Coordinating complex multi-agent collaboration

Prerequisites

• Flow Nexus account with active session

• MCP server flow-nexus configured

• Sufficient rUv credits for workflow execution

Core Concepts

Workflow Patterns

Pattern Description Use Case

CI/CD Pipeline Test, build, deploy sequence Software deployment

Data Processing ETL with validation steps Data engineering

Multi-Stage Review Automated analysis + approval Code review

Event-Driven Reactive to external events Webhooks, notifications

Scheduled Time-based execution Recurring tasks

Conditional Branching logic and decisions Complex business rules

Execution Strategies

• Sequential: Steps run one after another

• Parallel: Independent steps run simultaneously

• Conditional: Steps execute based on conditions

Agent Assignment

Workflows can automatically assign optimal agents to tasks using:

• Explicit Assignment: Specify agent type per step

• Vector Similarity: AI-powered matching based on task requirements

MCP Tools Reference

Workflow Creation

mcp__flow-nexus__workflow_create({ name: "Workflow Name", description: "Workflow description", steps: [ { id: "step1", action: "action_name", agent: "agent_type", // Optional: auto-assigned if not specified config: {} // Step-specific configuration }, { id: "step2", action: "action_name", depends: ["step1"] // Dependencies on other steps } ], triggers: ["trigger1", "trigger2"], // Event triggers priority: 5, // Priority 0-10 metadata: {} // Additional metadata }) // Returns: { workflow_id, name, status, created_at }

Workflow Execution

mcp__flow-nexus__workflow_execute({ workflow_id: "workflow_id", input_data: { // Input data for execution key: "value" }, async: true // Execute asynchronously via queue }) // Returns: { execution_id, status, started_at }

Status and Monitoring

// Get workflow status mcp__flow-nexus__workflow_status({ workflow_id: "workflow_id", execution_id: "execution_id", // Optional: specific execution include_metrics: true }) // Returns: { status, progress, metrics, step_results }

// List all workflows mcp__flow-nexus__workflow_list({ status: "active", // Filter by status limit: 10, offset: 0 })

// Check message queue status mcp__flow-nexus__workflow_queue_status({ queue_name: "queue_name", // Optional: specific queue include_messages: true })

Agent Assignment

mcp__flow-nexus__workflow_agent_assign({ task_id: "task_id", agent_type: "coder", // Preferred agent type use_vector_similarity: true // Use AI matching }) // Returns: { agent_id, type, match_score }

Audit Trail

mcp__flow-nexus__workflow_audit_trail({ workflow_id: "workflow_id", start_time: "2026-01-01T00:00:00Z", limit: 50 }) // Returns: { events: [{ timestamp, action, user, details }] }

Usage Examples

Example 1: CI/CD Pipeline

// Create comprehensive CI/CD workflow const cicdWorkflow = await mcp__flow-nexus__workflow_create({ name: "Full CI/CD Pipeline", description: "Complete testing, building, and deployment workflow", steps: [ { id: "lint", action: "run_linter", agent: "code-analyzer", config: { strict: true } }, { id: "test", action: "run_tests", agent: "tester", config: { coverage_threshold: 80 }, depends: ["lint"] }, { id: "security_scan", action: "security_check", agent: "security-analyzer", depends: ["lint"] }, { id: "build", action: "build_app", agent: "builder", depends: ["test", "security_scan"] }, { id: "deploy_staging", action: "deploy", agent: "deployer", config: { environment: "staging" }, depends: ["build"] }, { id: "integration_tests", action: "run_integration_tests", agent: "tester", depends: ["deploy_staging"] }, { id: "deploy_prod", action: "deploy", agent: "deployer", config: { environment: "production" }, depends: ["integration_tests"] } ], triggers: ["push_to_main", "release_tag"], priority: 8 });

// Execute on push await mcp__flow-nexus__workflow_execute({ workflow_id: cicdWorkflow.workflow_id, input_data: { branch: "main", commit: "abc123", author: "developer@example.com" }, async: true });

// Monitor progress const status = await mcp__flow-nexus__workflow_status({ workflow_id: cicdWorkflow.workflow_id, include_metrics: true });

console.log(Progress: ${status.progress}%, Current step: ${status.current_step});

Example 2: Data Processing Pipeline

// ETL workflow with validation const etlWorkflow = await mcp__flow-nexus__workflow_create({ name: "Data ETL Pipeline", description: "Extract, transform, and load data with validation", steps: [ { id: "extract", action: "extract_data", agent: "data-extractor", config: { source: "s3://bucket/raw-data" } }, { id: "validate", action: "validate_schema", agent: "data-validator", depends: ["extract"] }, { id: "transform", action: "transform_data", agent: "data-transformer", config: { rules: ["normalize", "dedupe", "enrich"] }, depends: ["validate"] }, { id: "quality_check", action: "run_quality_checks", agent: "data-analyst", depends: ["transform"] }, { id: "load", action: "load_to_warehouse", agent: "data-loader", config: { target: "postgres://warehouse" }, depends: ["quality_check"] } ], triggers: ["schedule:0 2 * * *", "manual_trigger"] // Daily at 2 AM });

// Manual execution await mcp__flow-nexus__workflow_execute({ workflow_id: etlWorkflow.workflow_id, input_data: { date: "2026-01-02" } });

Example 3: Multi-Stage Code Review

// Automated code review workflow const reviewWorkflow = await mcp__flow-nexus__workflow_create({ name: "Automated Code Review", description: "Multi-stage code analysis and review", steps: [ { id: "static_analysis", action: "run_static_analysis", agent: "code-analyzer" }, { id: "security_review", action: "security_scan", agent: "security-reviewer", depends: ["static_analysis"] }, { id: "performance_review", action: "analyze_performance", agent: "perf-analyzer", depends: ["static_analysis"] }, { id: "ai_review", action: "ai_code_review", agent: "ai-reviewer", depends: ["static_analysis"] }, { id: "compile_report", action: "generate_report", agent: "report-generator", depends: ["security_review", "performance_review", "ai_review"] } ], triggers: ["pull_request_opened", "pull_request_updated"] });

// Assign optimal agent dynamically await mcp__flow-nexus__workflow_agent_assign({ task_id: "security_review_123", use_vector_similarity: true });

Example 4: Queue Management

// Check queue status const queueStatus = await mcp__flow-nexus__workflow_queue_status({ include_messages: true });

console.log(Pending messages: ${queueStatus.pending}); console.log(Processing: ${queueStatus.processing});

// Review audit trail const audit = await mcp__flow-nexus__workflow_audit_trail({ workflow_id: "workflow_id", limit: 100 });

for (const event of audit.events) { console.log(${event.timestamp}: ${event.action} by ${event.user}); }

Execution Checklist

• Define workflow steps and dependencies

• Assign or auto-assign agents to steps

• Configure triggers (events, schedules)

• Set workflow priority

• Create the workflow

• Execute with appropriate input data

• Monitor progress and step status

• Review audit trail for compliance

• Clean up or archive completed workflows

Best Practices

• Step Granularity: Break complex tasks into atomic steps for better monitoring

• Dependency Chains: Carefully plan dependencies to maximize parallelism

• Error Handling: Include retry logic and fallback steps

• Async Execution: Use async mode for long-running workflows

• Agent Matching: Leverage vector similarity for optimal agent assignment

• Audit Compliance: Regularly review audit trails for security

Error Handling

Error Cause Solution

workflow_create_failed

Invalid step configuration Verify step IDs and dependencies

execution_failed

Step error or timeout Check step logs, increase timeout

agent_assignment_failed

No suitable agent available Specify alternative agent type

queue_overflow

Too many pending messages Scale workers or reduce rate

circular_dependency

Steps reference each other Review dependency graph

Metrics & Success Criteria

• Workflow Completion Rate: Target >95%

• Average Execution Time: Track per workflow type

• Queue Latency: <10 seconds for async jobs

• Agent Utilization: >80% during active workflows

• Error Rate: <5% per workflow type

Integration Points

With Swarms

// Swarm-powered workflow const swarm = await mcp__flow-nexus__swarm_init({ topology: "mesh" });

await mcp__flow-nexus__workflow_create({ name: "Swarm Workflow", steps: [ { id: "task", action: "swarm_execute", config: { swarm_id: swarm.swarm_id } } ] });

With Sandboxes

// Sandbox execution in workflow await mcp__flow-nexus__workflow_create({ name: "Sandbox Pipeline", steps: [ { id: "create", action: "sandbox_create", config: { template: "node" } }, { id: "test", action: "sandbox_execute", depends: ["create"] }, { id: "cleanup", action: "sandbox_delete", depends: ["test"] } ] });

Related Skills

• cloud-swarm - Multi-agent orchestration

• cloud-sandbox - Isolated execution environments

• cloud-neural - Neural network operations

References

• Flow Nexus Workflow Documentation

• Event-Driven Architecture Patterns

Version History

• 1.0.0 (2026-01-02): Initial release - converted from flow-nexus-workflow agent