AEM + AI Content Generation Workflows

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AEM + AI Content Generation Workflows: Automate Content at Scale

Introduction

Content teams using AEM are under constant pressure to produce more content, faster, and in multiple languages. With the rise of LLMs like OpenAI's GPT-4 and Anthropic's Claude, it is now practical to integrate AI content generation directly into AEM workflows — automating first drafts, metadata, translations, and content fragment creation.

In this post, we'll build a practical AEM Workflow that calls an external AI API, generates content, and writes it back into AEM Content Fragments.

Architecture

AEM Workflow Trigger (Page / Asset event)
        ↓
Custom Workflow Process Step (OSGi)
        ↓
HTTP Call → OpenAI / Claude API
        ↓
Parse AI Response
        ↓
Write to AEM Content Fragment / Page Properties

Step 1: Store Your API Key Securely in AEM

Never hardcode API keys. Store them as Cloud Manager environment variables and read them via OSGi config.

OSGi Config — AI Service Configuration

<!-- /apps/your-project/config.prod/com.yourproject.ai.AIServiceConfig.xml -->
<?xml version="1.0" encoding="UTF-8"?>
<jcr:root xmlns:sling="http://sling.apache.org/jcr/sling/1.0"
          xmlns:jcr="http://www.jcp.org/jcr/1.0"
    jcr:primaryType="sling:OsgiConfig"
    ai.api.endpoint="https://api.openai.com/v1/chat/completions"
    ai.api.key="$[env:OPENAI_API_KEY]"
    ai.model="gpt-4o"
    ai.max.tokens="{Long}1000"/>

OSGi Service to Read Config

@ObjectClassDefinition(name = "AI Service Configuration")
public @interface AIServiceConfig {
    String ai_api_endpoint() default "https://api.openai.com/v1/chat/completions";
    String ai_api_key() default "";
    String ai_model() default "gpt-4o";
    long ai_max_tokens() default 1000;
}

Step 2: Build the AI Client Service

@Component(service = AIContentService.class)
@Designate(ocd = AIServiceConfig.class)
public class AIContentServiceImpl implements AIContentService {

    private static final Logger log = LoggerFactory.getLogger(AIContentServiceImpl.class);
    private AIServiceConfig config;

    @Activate
    protected void activate(AIServiceConfig config) {
        this.config = config;
    }

    @Override
    public String generateContent(String prompt) {
        try {
            HttpClient client = HttpClient.newHttpClient();

            // Build request JSON
            String requestBody = buildRequestBody(prompt);

            HttpRequest request = HttpRequest.newBuilder()
                .uri(URI.create(config.ai_api_endpoint()))
                .header("Authorization", "Bearer " + config.ai_api_key())
                .header("Content-Type", "application/json")
                .POST(HttpRequest.BodyPublishers.ofString(requestBody))
                .timeout(Duration.ofSeconds(30))
                .build();

            HttpResponse<String> response = client.send(
                request, HttpResponse.BodyHandlers.ofString());

            if (response.statusCode() == 200) {
                return parseResponse(response.body());
            } else {
                log.error("AI API error: {}", response.body());
            }
        } catch (Exception e) {
            log.error("Failed to call AI API", e);
        }
        return null;
    }

    private String buildRequestBody(String prompt) {
        return "{"
            + "\"model\": \"" + config.ai_model() + "\","
            + "\"messages\": ["
            + "  {\"role\": \"system\", \"content\": \"You are an expert AEM content writer.\"},"
            + "  {\"role\": \"user\", \"content\": \"" + escapeJson(prompt) + "\"}"
            + "],"
            + "\"max_tokens\": " + config.ai_max_tokens()
            + "}";
    }

    private String parseResponse(String responseBody) throws Exception {
        // Simple JSON parse — use Gson/Jackson in production
        int start = responseBody.indexOf("\"content\":\"") + 11;
        int end = responseBody.indexOf("\"", start);
        return responseBody.substring(start, end);
    }

    private String escapeJson(String input) {
        return input.replace("\"", "\\\"").replace("\n", "\\n");
    }
}

Step 3: Create the Workflow Process Step

@Component(
    service = WorkflowProcess.class,
    property = {
        "process.label=AI Content Generation Step",
        "service.description=Generates content using AI and writes to Content Fragment"
    }
)
public class AIContentGenerationStep implements WorkflowProcess {

    @Reference
    private AIContentService aiContentService;

    @Reference
    private ResourceResolverFactory resolverFactory;

    @Override
    public void execute(WorkItem workItem, WorkflowSession wfSession,
                        MetaDataMap metaDataMap) throws WorkflowException {

        String payloadPath = workItem.getWorkflowData()
            .getPayload().toString();

        // Get topic from workflow metadata or page property
        String topic = metaDataMap.get("topic", "AEM Best Practices");
        String prompt = "Write a 200-word introduction about: " + topic
            + ". Use a professional tone suitable for a developer blog.";

        String generatedContent = aiContentService.generateContent(prompt);

        if (generatedContent != null) {
            writeToContentFragment(payloadPath, generatedContent);
        }
    }

    private void writeToContentFragment(String fragmentPath, String content) {
        Map<String, Object> param = new HashMap<>();
        param.put(ResourceResolverFactory.SUBSERVICE, "workflowService");

        try (ResourceResolver resolver = resolverFactory
                .getServiceResourceResolver(param)) {

            Resource fragmentResource = resolver.getResource(
                fragmentPath + "/jcr:content/data/master");

            if (fragmentResource != null) {
                ModifiableValueMap props = fragmentResource
                    .adaptTo(ModifiableValueMap.class);
                props.put("bodyText", content);
                props.put("ai_generated", true);
                props.put("ai_generated_date",
                    Calendar.getInstance());
                resolver.commit();
            }
        } catch (Exception e) {
            // handle exception
        }
    }
}

Step 4: Register Workflow Model in JCR

<!-- /conf/your-project/settings/workflow/models/ai-content-generation/jcr:content -->
<jcr:root xmlns:jcr="http://www.jcp.org/jcr/1.0"
          xmlns:cq="http://www.day.com/jcr/cq/1.0"
    jcr:primaryType="cq:WorkflowModel"
    jcr:title="AI Content Generation Workflow"
    description="Generates content using LLM API and saves to Content Fragment">

    <flow jcr:primaryType="cq:WorkflowNode"
          type="START"/>

    <aiStep jcr:primaryType="cq:WorkflowNode"
            type="PROCESS"
            process="com.yourproject.workflow.AIContentGenerationStep">
        <metaData jcr:primaryType="nt:unstructured"
                  topic="AEM Cloud Migration"/>
    </aiStep>

    <end jcr:primaryType="cq:WorkflowNode"
         type="END"/>
</jcr:root>

Step 5: Trigger Workflow via REST API

You can trigger this workflow programmatically (e.g., from a CI/CD pipeline or external system):

curl -X POST \
  "http://localhost:4502/api/workflow/instances" \
  -u admin:admin \
  -d "model=/conf/your-project/settings/workflow/models/ai-content-generation" \
  -d "payloadType=JCR_PATH" \
  -d "payload=/content/dam/your-project/content-fragments/article-1"

Bonus: Auto-Generate SEO Metadata with AI

Add a second workflow step that generates meta title and description:

String seoPrompt = "Generate an SEO meta title (max 60 chars) and "
    + "meta description (max 155 chars) for an article about: " + topic
    + ". Return as JSON: {\"title\": \"\", \"description\": \"\"}";

String seoJson = aiContentService.generateContent(seoPrompt);
// Parse and write to page jcr:content/metadata

Key Takeaways

  • Always store API keys in Cloud Manager environment variables, never in code.
  • Add a ai_generated flag to every node written by AI — useful for auditing and compliance.
  • Use workflow metadata to pass dynamic prompts, so the same workflow can serve multiple content types.
  • Set a reasonable timeout (30s) on your HTTP client — LLM APIs can be slow under load.
  • Consider a retry mechanism with exponential backoff for production workflows.

What's Next?

Next up: Edge Delivery Services + AI — how to combine AEM's modern delivery layer with AI-powered content generation and personalization.

Published on aemrules.com | Tags: AEM, AI, OpenAI, Content Generation, AEM Workflow, Content Fragments, LLM

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