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An automated feedback processing pipeline that analyzes sentiment with AI, categorizes issues, and creates tickets for negative feedback. This example shows how to combine workflows with agents and connectors for a hands-off analysis system.

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Project Structure

feedback-analysis/
├── main.ts
├── workflows/
│   └── analyze-feedback.ts
├── agents/
│   ├── sentiment.ts
│   └── action-recommender.ts
├── config/
│   └── settings.ts
├── .runflow/
│   └── rf.json
├── package.json
└── tsconfig.json

​
Step 1: Configuration

config/settings.ts
export const FEEDBACK_CATEGORIES = [
  'product_bug',
  'feature_request',
  'ux_issue',
  'performance',
  'documentation',
  'pricing',
  'support_quality',
  'praise',
  'other',
] as const;

export type FeedbackCategory = (typeof FEEDBACK_CATEGORIES)[number];

export const SENTIMENT_THRESHOLDS = {
  negative: -0.3, // Below this = negative
  positive: 0.3,  // Above this = positive
};

​
Step 2: Analysis Agents

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Sentiment Analyzer

Cheap, fast model for classification:
agents/sentiment.ts
import { Agent, openai } from '@runflow-ai/sdk';

export const sentimentAgent = new Agent({
  name: 'Sentiment Analyzer',
  instructions: `You analyze customer feedback for sentiment and categorization.

## Task
Analyze the feedback and return a structured analysis.

## Response Format
Respond with valid JSON only:
{
  "sentiment": "positive" | "neutral" | "negative",
  "score": <number between -1.0 and 1.0>,
  "category": "<one of: product_bug, feature_request, ux_issue, performance, documentation, pricing, support_quality, praise, other>",
  "themes": ["<key theme 1>", "<key theme 2>"],
  "summary": "<one sentence summary of the feedback>"
}`,
  model: openai('gpt-4o-mini'),
  modelConfig: { temperature: 0 },
});

​
Action Recommender

Better model for nuanced recommendations:
agents/action-recommender.ts
import { Agent, openai } from '@runflow-ai/sdk';

export const actionAgent = new Agent({
  name: 'Action Recommender',
  instructions: `You recommend specific actions based on customer feedback analysis.

## Task
Given the original feedback and sentiment analysis, recommend concrete actions.

## Response Format
Respond with valid JSON:
{
  "priority": "critical" | "high" | "medium" | "low",
  "actions": [
    { "action": "<what to do>", "owner": "<team: product | engineering | support | marketing>" }
  ],
  "shouldFollowUp": <boolean>,
  "followUpMessage": "<suggested response to the customer, if applicable>"
}`,
  model: openai('gpt-4o'),
  modelConfig: { temperature: 0.3 },
});

​
Step 3: Workflow

The workflow chains analysis steps and conditionally creates tickets:
workflows/analyze-feedback.ts
import { createWorkflow } from '@runflow-ai/sdk';
import { z } from 'zod';
import { sentimentAgent } from '../agents/sentiment';
import { actionAgent } from '../agents/action-recommender';

export const analyzeFeedbackWorkflow = createWorkflow({
  id: 'analyze-feedback',
  inputSchema: z.object({
    feedback: z.string(),
    customerEmail: z.string(),
    customerName: z.string(),
    source: z.string(),
  }),
  outputSchema: z.any(),
})
  // Step 1: Analyze sentiment
  .agent('analyze', sentimentAgent, {
    promptTemplate: `Analyze this customer feedback:

"{{input.feedback}}"

Customer: {{input.customerName}} ({{input.customerEmail}})
Source: {{input.source}}`,
  })

  // Step 2: Recommend actions
  .agent('recommend', actionAgent, {
    promptTemplate: `Original feedback: "{{input.feedback}}"
Customer: {{input.customerName}}

Sentiment analysis:
{{analyze.text}}

What actions should we take?`,
  })

  // Step 3: Create ticket if negative
  .condition(
    'check-negative',
    (ctx) => {
      try {
        const analysis = JSON.parse(ctx.stepResults.get('analyze').text);
        return analysis.sentiment === 'negative';
      } catch {
        return false;
      }
    },
    // Negative path: create ticket
    [
      {
        id: 'create-ticket',
        type: 'connector',
        config: {
          connector: 'hubspot',
          resource: 'tickets',
          action: 'create',
          parameters: {
            subject: 'Negative Feedback — {{input.customerName}}',
            content: `Feedback: {{input.feedback}}

Analysis: {{analyze.text}}

Recommended actions: {{recommend.text}}`,
            priority: 'high',
            category: 'feedback',
          },
        },
      },
    ],
    // Positive/neutral path: log only
    [
      {
        id: 'log-feedback',
        type: 'function',
        config: {
          execute: async (input, ctx) => {
            return { logged: true, sentiment: 'positive_or_neutral' };
          },
        },
      },
    ]
  )
  .build();

​
Step 4: Main Entry Point

main.ts
import { identify, track } from '@runflow-ai/sdk/observability';
import { analyzeFeedbackWorkflow } from './workflows/analyze-feedback';

export async function main(input: any) {
  // Validate
  if (!input?.feedback) {
    return { error: 'feedback is required' };
  }

  identify(input.customerEmail || input.customerId || 'anonymous');

  try {
    const result = await analyzeFeedbackWorkflow.execute({
      feedback: input.feedback,
      customerEmail: input.customerEmail || '',
      customerName: input.customerName || 'Unknown',
      source: input.source || 'api',
    });

    // Parse results for metrics
    let sentiment = 'unknown';
    let category = 'unknown';
    try {
      const analysis = JSON.parse(result.stepResults?.analyze?.text || '{}');
      sentiment = analysis.sentiment;
      category = analysis.category;
    } catch {}

    // Track feedback metrics
    track('feedback_analyzed', {
      sentiment,
      category,
      source: input.source || 'api',
      ticketCreated: sentiment === 'negative',
    });

    return {
      message: `Feedback analyzed: ${sentiment} sentiment, category: ${category}`,
      analysis: result.stepResults?.analyze?.text,
      actions: result.stepResults?.recommend?.text,
      ticketCreated: sentiment === 'negative',
    };
  } catch (error) {
    console.error('[feedback-analysis] Error:', error);
    return { error: 'Failed to analyze feedback' };
  }
}

​
Triggering the Pipeline

This workflow is typically triggered by external events, not user conversations: 
# From a webhook (NPS survey, support form, review platform)
curl -X POST https://your-agent.runflow.ai/api \
  -H "Content-Type: application/json" \
  -d '{
    "feedback": "The product crashes every time I try to export. Very frustrated.",
    "customerEmail": "jane@example.com",
    "customerName": "Jane Doe",
    "source": "nps_survey"
  }'

​
How It Works

Feedback arrives (webhook, form, API)
       ↓
┌────────────────────┐
│  Sentiment Agent    │  gpt-4o-mini → sentiment, category, themes
└──────┬─────────────┘
       ↓
┌────────────────────┐
│  Action Agent       │  gpt-4o → priority, actions, follow-up
└──────┬─────────────┘
       ↓
   Negative?
  ╱         ╲
 Yes         No
  ↓           ↓
Create     Log for
HubSpot    analytics
ticket
  ↓
Track metrics

​
Key Patterns

​
Pipeline vs Conversation

This is a workflow (pipeline), not an agent (conversation). Data flows in, gets processed, and comes out — no back-and-forth with a user. Workflows are ideal for batch processing and event-driven automations.

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Cheap Classification, Quality Recommendations

The sentiment agent uses gpt-4o-mini (fast, cheap). The action recommender uses gpt-4o (better reasoning). Match model cost to task complexity.

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Structured JSON for Reliable Branching

Both agents return structured JSON so the workflow can reliably branch on results: 
// Agent returns: { "sentiment": "negative", ... }
// Workflow condition parses and branches:
.condition('check', (ctx) => {
  const analysis = JSON.parse(ctx.stepResults.get('analyze').text);
  return analysis.sentiment === 'negative';
})

​
Next Steps

Workflows

Learn more about workflows

Connectors

HubSpot, Slack integrations

Multi-Agent System

Supervisor pattern

Observability

Track business metrics