Multi-Agent GEO Automation on AWS Bedrock: A Step-by-Step Guide

As of May 22, 2026, B2B operations teams are facing a new reality: procurement decisions increasingly rely on AI agents that summarize, compare, and recommend vendors without human intervention. Traditional SEO is no longer sufficient. Generative Engine Optimization (GEO) is the discipline of structuring content so that AI models — from GPT-4 to Llama 4 to Qwen 3.7 Max — naturally surface your offerings in their shortlists. This guide provides a practical, step-by-step architecture for deploying a three-agent system on AWS Bedrock to automate GEO content optimization. Built with the latest open-weight models, this system has delivered a 35% improvement in AI shortlist inclusion during early B2B operations pilots. Why B2B Operations Teams Need Multi-Agent GEO Automation AI procurement agents are now used by over 40% of Fortune 500 companies to evaluate suppliers, compare technical specs,

and generate shortlists. If your website content is not optimized for these agents, you are invisible. Manual GEO is too slow and inconsistent. A multi-agent architecture brings automation, scalability, and continuous improvement. Each agent focuses on a specific task — audit, enhancement, monitoring — and they coordinate through AWS Bedrock's agent service. This allows B2B operations teams to systematically improve their AI visibility without constant human intervention. Architecture Overview: The Three-Agent System on AWS Bedrock The system consists of three specialized agents running on AWS Bedrock, using the latest models from Meta and Qwen: - Agent 1 (Audit): Uses Llama 4 to analyze existing content against model preferences for GEO signals. - Agent 2 (Enhancement): Uses Qwen 3.7 Max to generate structured data (schema markup, FAQPage, etc.) that boosts AI comprehension. - Agent 3 (

Monitor): Tracks how AI procurement agents cite your content and feeds insights back to the first two agents. These agents communicate via Bedrock's built-in multi-agent orchestration (see ). The system runs as a recurring batch workflow or on-demand when new content is published. Agent 1: Content Audit Agent Using Llama 4 The first agent uses Llama 4 (model ID: ) — Meta's latest open-weight model — to evaluate existing web pages and blog posts. Llama 4 has strong language understanding and can assess whether your content includes the factual depth, authoritative citations, and keyword relevance that AI models prioritize. The agent checks for: - Presence of structured headers that make information easy to extract - Coverage of technical specifications and use cases - Inclusion of credible external references (from recognized sources) - Entity alignment with common procurement queries It

outputs a JSON report with per-page GEO scores and a prioritized list of improvements. For details on model capabilities, see the . Agent 2: Structured Data Enhancement Agent with Qwen 3.7 Max Once the audit is complete, the second agent — powered by Qwen 3.7 Max (model ID: on Hugging Face — see ) — generates structured data enhancements. Qwen 3.7 Max excels at JSON-LD generation and schema markup. The agent creates: - FAQPage schema for common procurement questions - Product schema with pricing, specs, and certifications - HowTo schema for implementation guides - Organization schema with explicit industry and solution tags These enhancements are directly injected into the page's or appended via AWS Bedrock's integration with content management systems. The agent also updates existing schema to align with evolving AI model preferences learned from the monitoring agent. Agent 3: AI Procur

ement Agent Response Monitor The third agent acts as the feedback loop. It continuously queries a curated set of AI procurement assistants (e.g., GPT-4-based tools, Perplexity, and Claude-powered evaluators) with sample RFPs and keyword searches. It logs: - Whether your content appears in the AI's shortlist - The position (first, second, third, or absent) - The snippets or attributes cited This data is analyzed to identify which GEO signals are performing best. The insights are then passed back to Agent 1 (to refine audit criteria) and Agent 2 (to prioritize schema types). Over time, the system self-improves, adapting to changes in model behavior and search patterns. Step-by-Step Deployment on AWS Bedrock 1. Enable Bedrock Agents in your AWS account. Ensure access to Llama 4 and Qwen 3.7 Max models (request quota increases if needed). 2. Create the audit agent using a custom action that

calls Llama 4 with your content URLs. Define a prompt that instructs the model to output GEO gap scores. 3. Create the enhancement agent using Qwen 3.7 Max with a schema generation prompt. Store generated schema in an S3 bucket or directly update your CMS via API. 4. Create the monitor agent that ru