translation/source/documents/course/04-workflows/12-using-agent-in-workflow.md

Using Agent in Workflow

Now you'll create a workflow step that uses your AI agent to provide intelligent content analysis.

In each step, in the execute function, you have access to the mastra class which provides you the ability to access Agents, Tools, and even other Workflows. In this case, we use the mastra class to get our agent and call that agent's generate() function.

Creating an AI Analysis Step

Add this step to your workflow file:

const aiAnalysisStep = createStep({
  id: "ai-analysis",
  description: "AI-powered content analysis",
  inputSchema: z.object({
    content: z.string(),
    type: z.string(),
    wordCount: z.number(),
    metadata: z.object({
      readingTime: z.number(),
      difficulty: z.enum(["easy", "medium", "hard"]),
      processedAt: z.string(),
    }),
    summary: z.string(),
  }),
  outputSchema: z.object({
    content: z.string(),
    type: z.string(),
    wordCount: z.number(),
    metadata: z.object({
      readingTime: z.number(),
      difficulty: z.enum(["easy", "medium", "hard"]),
      processedAt: z.string(),
    }),
    summary: z.string(),
    aiAnalysis: z.object({
      score: z.number(),
      feedback: z.string(),
    }),
  }),
  execute: async ({ inputData, mastra }) => {
    const { content, type, wordCount, metadata, summary } = inputData;

    // Create prompt for the AI agent
    const prompt = `
Analyze this ${type} content:

Content: "${content}"
Word count: ${wordCount}
Reading time: ${metadata.readingTime} minutes
Difficulty: ${metadata.difficulty}

Please provide:
1. A quality score from 1-10
2. Brief feedback on strengths and areas for improvement

Format as JSON: {"score": number, "feedback": "your feedback here"}
    `;

    // Get the contentAgent from the mastra instance.
    const contentAgent = mastra.getAgent("contentAgent");
    const { text } = await contentAgent.generate([
      { role: "user", content: prompt },
    ]);

    // Parse AI response (with fallback)
    let aiAnalysis;
    try {
      aiAnalysis = JSON.parse(text);
    } catch {
      aiAnalysis = {
        score: 7,
        feedback: "AI analysis completed. " + text,
      };
    }

    console.log(`🤖 AI Score: ${aiAnalysis.score}/10`);

    return {
      content,
      type,
      wordCount,
      metadata,
      summary,
      aiAnalysis,
    };
  },
});

Your agent-powered step is ready! Next, you'll add it to your workflow for complete AI-enhanced content processing.