---
title: Agentic patterns
description: Implement common AI agent patterns like prompt chaining, routing, parallelization, and orchestrator-workers on Cloudflare.
image: https://edgetunnel-b2h.pages.dev/dev-products-preview.png
---

> Documentation Index  
> Fetch the complete documentation index at: https://edgetunnel-b2h.pages.dev/agents/llms.txt  
> Use this file to discover all available pages before exploring further. 

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# Agentic patterns

This page lists and defines common patterns for implementing AI agents, based on [Anthropic's patterns for building effective agents ↗](https://www.anthropic.com/research/building-effective-agents).

Code samples use the [AI SDK ↗](https://ai-sdk.dev/docs/foundations/agents), running in [Durable Objects](https://edgetunnel-b2h.pages.dev/durable-objects).

## Prompt Chaining

Decomposes tasks into a sequence of steps, where each LLM call processes the output of the previous one.

![Figure 1: Prompt Chaining](https://edgetunnel-b2h.pages.dev/_astro/01-prompt-chaining.BLijYLLo_Z4mjQb.webp) 

**TypeScript**

```ts
import { openai } from "@ai-sdk/openai";
import { generateText, generateObject } from "ai";
import { z } from "zod";


export default async function generateMarketingCopy(input: string) {
  const model = openai("gpt-4o");


  // First step: Generate marketing copy
  const { text: copy } = await generateText({
    model,
    prompt: `Write persuasive marketing copy for: ${input}. Focus on benefits and emotional appeal.`,
  });


  // Perform quality check on copy
  const { object: qualityMetrics } = await generateObject({
    model,
    schema: z.object({
      hasCallToAction: z.boolean(),
      emotionalAppeal: z.number().min(1).max(10),
      clarity: z.number().min(1).max(10),
    }),
    prompt: `Evaluate this marketing copy for:
    1. Presence of call to action (true/false)
    2. Emotional appeal (1-10)
    3. Clarity (1-10)


    Copy to evaluate: ${copy}`,
  });


  // If quality check fails, regenerate with more specific instructions
  if (
    !qualityMetrics.hasCallToAction ||
    qualityMetrics.emotionalAppeal < 7 ||
    qualityMetrics.clarity < 7
  ) {
    const { text: improvedCopy } = await generateText({
      model,
      prompt: `Rewrite this marketing copy with:
      ${!qualityMetrics.hasCallToAction ? "- A clear call to action" : ""}
      ${qualityMetrics.emotionalAppeal < 7 ? "- Stronger emotional appeal" : ""}
      ${qualityMetrics.clarity < 7 ? "- Improved clarity and directness" : ""}


      Original copy: ${copy}`,
    });
    return { copy: improvedCopy, qualityMetrics };
  }


  return { copy, qualityMetrics };
}
```

## Routing

Classifies input and directs it to specialized followup tasks, allowing for separation of concerns.

![Figure 2: Routing](https://edgetunnel-b2h.pages.dev/_astro/2_Routing.CT-Tgwab_1YYXmR.webp) 

**TypeScript**

```ts
import { openai } from '@ai-sdk/openai';
import { generateObject, generateText } from 'ai';
import { z } from 'zod';


async function handleCustomerQuery(query: string) {
  const model = openai('gpt-4o');


  // First step: Classify the query type
  const { object: classification } = await generateObject({
    model,
    schema: z.object({
      reasoning: z.string(),
      type: z.enum(['general', 'refund', 'technical']),
      complexity: z.enum(['simple', 'complex']),
    }),
    prompt: `Classify this customer query:
    ${query}


    Determine:
    1. Query type (general, refund, or technical)
    2. Complexity (simple or complex)
    3. Brief reasoning for classification`,
  });


  // Route based on classification
  // Set model and system prompt based on query type and complexity
  const { text: response } = await generateText({
    model:
      classification.complexity === 'simple'
        ? openai('gpt-4o-mini')
        : openai('o1-mini'),
    system: {
      general:
        'You are an expert customer service agent handling general inquiries.',
      refund:
        'You are a customer service agent specializing in refund requests. Follow company policy and collect necessary information.',
      technical:
        'You are a technical support specialist with deep product knowledge. Focus on clear step-by-step troubleshooting.',
    }[classification.type],
    prompt: query,
  });


  return { response, classification };
}
```

## Parallelization

Enables simultaneous task processing through sectioning or voting mechanisms.

![Figure 3: Parallelization](https://edgetunnel-b2h.pages.dev/_astro/3_Parallelization.gkwf-xnL_1psyLV.webp) 

**TypeScript**

```ts
import { openai } from '@ai-sdk/openai';
import { generateText, generateObject } from 'ai';
import { z } from 'zod';


// Example: Parallel code review with multiple specialized reviewers
async function parallelCodeReview(code: string) {
  const model = openai('gpt-4o');


  // Run parallel reviews
  const [securityReview, performanceReview, maintainabilityReview] =
    await Promise.all([
      generateObject({
        model,
        system:
          'You are an expert in code security. Focus on identifying security vulnerabilities, injection risks, and authentication issues.',
        schema: z.object({
          vulnerabilities: z.array(z.string()),
          riskLevel: z.enum(['low', 'medium', 'high']),
          suggestions: z.array(z.string()),
        }),
        prompt: `Review this code:
      ${code}`,
      }),


      generateObject({
        model,
        system:
          'You are an expert in code performance. Focus on identifying performance bottlenecks, memory leaks, and optimization opportunities.',
        schema: z.object({
          issues: z.array(z.string()),
          impact: z.enum(['low', 'medium', 'high']),
          optimizations: z.array(z.string()),
        }),
        prompt: `Review this code:
      ${code}`,
      }),


      generateObject({
        model,
        system:
          'You are an expert in code quality. Focus on code structure, readability, and adherence to best practices.',
        schema: z.object({
          concerns: z.array(z.string()),
          qualityScore: z.number().min(1).max(10),
          recommendations: z.array(z.string()),
        }),
        prompt: `Review this code:
      ${code}`,
      }),
    ]);


  const reviews = [
    { ...securityReview.object, type: 'security' },
    { ...performanceReview.object, type: 'performance' },
    { ...maintainabilityReview.object, type: 'maintainability' },
  ];


  // Aggregate results using another model instance
  const { text: summary } = await generateText({
    model,
    system: 'You are a technical lead summarizing multiple code reviews.',
    prompt: `Synthesize these code review results into a concise summary with key actions:
    ${JSON.stringify(reviews, null, 2)}`,
  });


  return { reviews, summary };
}
```

## Orchestrator-Workers

A central LLM dynamically breaks down tasks, delegates to Worker LLMs, and synthesizes results.

![Figure 4: Orchestrator Workers](https://edgetunnel-b2h.pages.dev/_astro/4_Orchestrator-Workers.jVghtZEj_Z6FePI.webp) 

**TypeScript**

```ts
import { openai } from '@ai-sdk/openai';
import { generateObject } from 'ai';
import { z } from 'zod';


async function implementFeature(featureRequest: string) {
  // Orchestrator: Plan the implementation
  const { object: implementationPlan } = await generateObject({
    model: openai('o1'),
    schema: z.object({
      files: z.array(
        z.object({
          purpose: z.string(),
          filePath: z.string(),
          changeType: z.enum(['create', 'modify', 'delete']),
        }),
      ),
      estimatedComplexity: z.enum(['low', 'medium', 'high']),
    }),
    system:
      'You are a senior software architect planning feature implementations.',
    prompt: `Analyze this feature request and create an implementation plan:
    ${featureRequest}`,
  });


  // Workers: Execute the planned changes
  const fileChanges = await Promise.all(
    implementationPlan.files.map(async file => {
      // Each worker is specialized for the type of change
      const workerSystemPrompt = {
        create:
          'You are an expert at implementing new files following best practices and project patterns.',
        modify:
          'You are an expert at modifying existing code while maintaining consistency and avoiding regressions.',
        delete:
          'You are an expert at safely removing code while ensuring no breaking changes.',
      }[file.changeType];


      const { object: change } = await generateObject({
        model: openai('gpt-4o'),
        schema: z.object({
          explanation: z.string(),
          code: z.string(),
        }),
        system: workerSystemPrompt,
        prompt: `Implement the changes for ${file.filePath} to support:
        ${file.purpose}


        Consider the overall feature context:
        ${featureRequest}`,
      });


      return {
        file,
        implementation: change,
      };
    }),
  );


  return {
    plan: implementationPlan,
    changes: fileChanges,
  };
}
```

## Evaluator-Optimizer

One LLM generates responses while another provides evaluation and feedback in a loop.

![Figure 5: Evaluator-Optimizer](https://edgetunnel-b2h.pages.dev/_astro/5_Evaluator-Optimizer.uXTWfJxj_Z8n6xm.webp) 

**TypeScript**

```ts
import { openai } from '@ai-sdk/openai';
import { generateText, generateObject } from 'ai';
import { z } from 'zod';


async function translateWithFeedback(text: string, targetLanguage: string) {
  let currentTranslation = '';
  let iterations = 0;
  const MAX_ITERATIONS = 3;


  // Initial translation
  const { text: translation } = await generateText({
    model: openai('gpt-4o-mini'), // use small model for first attempt
    system: 'You are an expert literary translator.',
    prompt: `Translate this text to ${targetLanguage}, preserving tone and cultural nuances:
    ${text}`,
  });


  currentTranslation = translation;


  // Evaluation-optimization loop
  while (iterations < MAX_ITERATIONS) {
    // Evaluate current translation
    const { object: evaluation } = await generateObject({
      model: openai('gpt-4o'), // use a larger model to evaluate
      schema: z.object({
        qualityScore: z.number().min(1).max(10),
        preservesTone: z.boolean(),
        preservesNuance: z.boolean(),
        culturallyAccurate: z.boolean(),
        specificIssues: z.array(z.string()),
        improvementSuggestions: z.array(z.string()),
      }),
      system: 'You are an expert in evaluating literary translations.',
      prompt: `Evaluate this translation:


      Original: ${text}
      Translation: ${currentTranslation}


      Consider:
      1. Overall quality
      2. Preservation of tone
      3. Preservation of nuance
      4. Cultural accuracy`,
    });


    // Check if quality meets threshold
    if (
      evaluation.qualityScore >= 8 &&
      evaluation.preservesTone &&
      evaluation.preservesNuance &&
      evaluation.culturallyAccurate
    ) {
      break;
    }


    // Generate improved translation based on feedback
    const { text: improvedTranslation } = await generateText({
      model: openai('gpt-4o'), // use a larger model
      system: 'You are an expert literary translator.',
      prompt: `Improve this translation based on the following feedback:
      ${evaluation.specificIssues.join('\n')}
      ${evaluation.improvementSuggestions.join('\n')}


      Original: ${text}
      Current Translation: ${currentTranslation}`,
    });


    currentTranslation = improvedTranslation;
    iterations++;
  }


  return {
    finalTranslation: currentTranslation,
    iterationsRequired: iterations,
  };
}
```

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