Workflow Orchestration

Part of Agent Skills™ by googleadsagent.ai™

Description

Workflow Orchestration provides a visual AI flow-building framework with MCP plugin support, conditional branching, parallel execution, and error recovery. The agent designs and implements multi-step workflows that chain AI operations, data transformations, and external service calls into reliable, repeatable automation pipelines.

Individual AI calls are useful; orchestrated workflows are transformative. A content pipeline that drafts text, fact-checks claims, generates images, formats for multiple platforms, and schedules publication—all triggered by a single input—replaces hours of manual coordination. This skill encodes the patterns for building such workflows: DAG-based execution, conditional routing, retry logic, and human-in-the-loop checkpoints.

The MCP (Model Context Protocol) plugin system extends workflows with external capabilities: database queries, file operations, API calls, and browser automation. Each MCP tool becomes a reusable node in the workflow graph, enabling agents to interact with any system that exposes an MCP interface. Workflows compose these nodes into complex automations without custom integration code.

Use When

• Building multi-step AI automation pipelines
• Chaining LLM calls with data transformations and API integrations
• Implementing conditional logic in AI workflows (if/else, switch)
• Adding human-in-the-loop approval steps to automated processes
• Integrating MCP tools into repeatable workflows
• Orchestrating parallel AI tasks with dependency management

How It Works

graph TD
    A[Trigger: Manual / Schedule / Webhook] --> B[Node 1: Input Parser]
    B --> C{Conditional Router}
    C -->|Type A| D[Node 2a: LLM Analysis]
    C -->|Type B| E[Node 2b: Database Query via MCP]
    D --> F[Node 3: Parallel Execution]
    E --> F
    F --> G[Node 3a: Image Generation]
    F --> H[Node 3b: Content Formatting]
    G --> I[Node 4: Human Review Checkpoint]
    H --> I
    I -->|Approved| J[Node 5: Publish via MCP]
    I -->|Rejected| K[Node 5: Return to Step 2]
    J --> L[Node 6: Log + Notify]

Workflows are directed acyclic graphs (DAGs) where each node is a discrete operation. The orchestrator manages execution order, passes data between nodes, handles retries on failure, and pauses at human checkpoints.

Implementation

interface WorkflowNode {
  id: string;
  type: "llm" | "mcp" | "transform" | "condition" | "human_review" | "parallel";
  config: Record<string, unknown>;
  next: string | string[] | ConditionalNext[];
  retries?: number;
  timeout_ms?: number;
}

interface ConditionalNext {
  condition: string;
  target: string;
}

interface Workflow {
  id: string;
  name: string;
  trigger: { type: "manual" | "schedule" | "webhook"; config: Record<string, unknown> };
  nodes: WorkflowNode[];
}

class WorkflowEngine {
  private state = new Map<string, unknown>();

  async execute(workflow: Workflow, input: unknown): Promise<unknown> {
    this.state.set("input", input);
    let currentNode = workflow.nodes[0];

    while (currentNode) {
      const result = await this.executeNode(currentNode);
      this.state.set(currentNode.id, result);

      const nextId = this.resolveNext(currentNode, result);
      currentNode = nextId ? workflow.nodes.find(n => n.id === nextId)! : undefined!;
    }

    return Object.fromEntries(this.state);
  }

  private async executeNode(node: WorkflowNode): Promise<unknown> {
    for (let attempt = 0; attempt <= (node.retries ?? 0); attempt++) {
      try {
        switch (node.type) {
          case "llm": return await this.executeLLM(node.config);
          case "mcp": return await this.executeMCP(node.config);
          case "transform": return this.executeTransform(node.config);
          case "parallel": return await this.executeParallel(node.config);
          case "human_review": return await this.waitForHumanReview(node.config);
          default: throw new Error(`Unknown node type: ${node.type}`);
        }
      } catch (error) {
        if (attempt === (node.retries ?? 0)) throw error;
        await this.delay(1000 * Math.pow(2, attempt));
      }
    }
  }

  private async executeMCP(config: Record<string, unknown>): Promise<unknown> {
    const { server, tool, arguments: args } = config as {
      server: string; tool: string; arguments: Record<string, unknown>;
    };
    return callMcpTool(server, tool, this.interpolate(args));
  }

  private async executeParallel(config: Record<string, unknown>): Promise<unknown[]> {
    const { nodeIds } = config as { nodeIds: string[] };
    const nodes = nodeIds.map(id => this.findNode(id));
    return Promise.all(nodes.map(n => this.executeNode(n)));
  }

  private interpolate(obj: Record<string, unknown>): Record<string, unknown> {
    return JSON.parse(
      JSON.stringify(obj).replace(/\{\{(\w+)\.(\w+)\}\}/g, (_, nodeId, key) => {
        const nodeResult = this.state.get(nodeId) as Record<string, unknown>;
        return String(nodeResult?.[key] ?? "");
      })
    );
  }
}

Best Practices

• Design workflows as DAGs—cycles indicate a design flaw, not a feature
• Set timeouts on every node to prevent indefinite hangs from external services
• Implement exponential backoff retries for transient failures (network, rate limits)
• Place human review checkpoints before irreversible actions (publish, delete, send)
• Log every node execution with input, output, duration, and attempt count
• Use MCP tools for external integrations rather than hardcoded API clients

Platform Compatibility

Related Skills

• Batch Processing
• AI Chat Studio
• Parallel Agent Orchestration
• MCP Server Creation

Keywords

workflow orchestration mcp dag parallel-execution conditional-routing human-in-the-loop automation

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