How Multi-Agent AI Is Transforming Advertising at Scale for CPG and Retail Brands on AWS

Consumer packaged goods (CPG) and retail companies manage advertising campaigns across dozens of product categories and hundreds of stock keeping units (SKUs). Seasonal promotions, Prime Day, back-to-school campaigns, and holiday sales add further complexity to advertising operations.

Marketing budgets and team sizes do not always grow at the same pace as campaign complexity. Analysts often need to switch between reporting dashboards, campaign management tools, internal documentation, and historical data before they can make and implement a decision.

Multi-agent artificial intelligence (AI) offers a new approach. Using Amazon Web Services (AWS), organizations can build systems in which specialized AI agents work together to analyze performance data, retrieve recommendations from internal knowledge bases, and execute authorized advertising operations.

Why Advertising Management Needs a New Approach

Consider a CPG brand advertising snacks, beverages, personal care products, and household essentials. Its Amazon advertising account may contain hundreds of active Sponsored Products campaigns.

If a campaign's return on ad spend (ROAS) drops by 22% after a competitor launches a coupon, an analyst may need to:

• Review the campaign's performance metrics.
• Retrieve historical bid data for specific Amazon Standard Identification Numbers (ASINs).
• Find internal recommendations for optimization during promotional periods.
• Adjust the daily budget and targeting settings.
• Verify that the changes have been applied successfully.

Completing this workflow manually can take around 20 minutes for a single campaign. When dozens of campaigns require regular analysis and optimization, the operational workload quickly becomes substantial.

According to a Gartner survey published in May 2026, AI automated 16% of marketing work at the time of the survey. The research also found that marketing leaders expected the share of marketing work automated by AI to reach 36% by 2028.

As advertising portfolios become more complex, multi-agent AI offers one way to reduce repetitive manual work and help marketing teams use their resources more efficiently.

A Different Model: One Conversation, Multiple Specialists

Instead of working through several systems sequentially, an analyst could submit a natural-language request:

"Show me the weekend performance of our snacks campaigns. Results declined after a competitor launched a coupon. Find our promotional defense recommendations and suggest changes to the budget and targeting."

A multi-agent system distributes the request among specialized agents. One retrieves advertising performance data, another searches internal guidance, and a third can prepare or execute campaign changes through available APIs, subject to its permissions and configured controls.

Amazon Bedrock can support the intelligent processing and routing of requests in this architecture. Rather than relying on a single general-purpose assistant, the system uses specialized agents coordinated by a central orchestrator.

The Amazon Ads Solutions Architecture team has demonstrated this approach in a hands-on workshop focused on building multi-agent systems on AWS.

Explore the Amazon Ads multi-agent AI workshop

The Architecture: A Central Orchestrator and Specialized Agents

The architecture follows a hub-and-spoke model. A central orchestration agent receives a natural-language request, interprets the task, and delegates it to the appropriate specialist agents.

• Campaign Management Agent. Creates, updates, and pauses campaigns and advertising profiles through the Amazon Advertising API and Amazon Ads MCP Server. It can handle operations such as budget changes, targeting adjustments, and campaign launches.
• Analytics and Reporting Agent. Retrieves impressions, clicks, advertising spend, ROAS, and share-of-voice metrics across campaigns, categories, and reporting periods. It supports regular business reviews and promotional performance monitoring.
• Knowledge Agent. Searches a curated library of advertising playbooks and best practices. For example, it can retrieve guidance on adjusting bids and reallocating budgets when competitors introduce promotional offers.
• Database Agent. Queries historical campaign data stored in Amazon Relational Database Service (Amazon RDS). It supports seasonal trend analysis, year-over-year comparisons, and reporting by category or individual product.

In this architecture, agents run in separate containers using Amazon Bedrock AgentCore. This enables components to scale independently. The Agent2Agent (A2A) protocol supports agent discovery and interaction based on published capability descriptions.

The architecture can be extended with additional specialists, such as a Seasonal Planning Agent. This approach makes it possible to add capabilities without completely redesigning the central orchestrator.

How Multi-Agent AI Works in Practice

Consider a typical advertising analyst workflow.

Step 1: Analyze campaign performance

Analyst: "Show me the weekend performance of our snacks and beverages campaigns."

The Analytics and Reporting Agent retrieves data from advertising reporting sources and returns ROAS, spend, and impression metrics broken down by category.

Step 2: Retrieve relevant recommendations

Analyst: "The snacks campaign declined after a competitor launched a coupon. What does our promotional defense playbook recommend?"

The Knowledge Agent searches the internal documentation and returns relevant guidance on bid adjustments and budget reallocation during competitive promotions.

Step 3: Prepare and execute campaign changes

Analyst: "Increase the daily budget for the snacks campaign to $1,200 and shift targeting toward 24-pack products."

The Campaign Management Agent calls the Amazon Advertising API and performs the authorized operations. The system can then confirm the result and report the changes made.

This approach brings data retrieval, recommendation discovery, and campaign management into a single conversational workflow. Amazon Bedrock AgentCore Memory can maintain context across multiple turns, eliminating the need for the analyst to repeatedly identify the campaign being discussed.

Actual execution depends on the integration configuration, available permissions, and any required approval procedures.

Potential Business Benefits of Multi-Agent AI

For marketing leaders and business decision-makers, the key question is how the technology can improve operational performance. The following figures are directional estimates based on the workflow described here, not measured results from a specific customer deployment.

1. Optimize More Campaigns With Existing Resources

In the example above, manual optimization takes around 20 minutes per campaign, while the multi-agent workflow can complete an equivalent sequence in under two minutes. This represents a potential tenfold increase in the number of campaigns an analyst can process within the same period.

Actual results depend on campaign complexity, data quality, approval requirements, and the degree of automation implemented.

2. Give Analysts More Time for Strategic Work

If each optimization saves approximately 18 minutes, reviewing 80 campaigns in a weekly cycle could reclaim around 24 hours. That is more than half a standard working week.

The time saved can be redirected toward higher-value activities, including creative testing, category planning, competitive analysis, and advertising strategy development.

3. Respond Faster to Competitive Changes

When a competitor launches a coupon or changes its advertising strategy, delays in responding can increase costs and reduce campaign efficiency. Shortening the time between identifying an issue and taking action can help teams adjust bids, budgets, and targeting more quickly.

This is particularly valuable during high-activity periods such as Prime Day, back-to-school campaigns, and holiday promotions. The actual impact on ROAS and revenue should be measured using each organization's campaign data.

4. Manage Advertising Across the Entire Portfolio

A multi-agent architecture distributes tasks among specialized components. This can help teams work across multiple categories and hundreds of SKUs without relying on sequential manual processing of every campaign.

Faster and more consistent optimization can also help brands maintain their share of voice in competitive product categories.

5. Apply Best Practices More Consistently

When advertising playbooks, seasonal strategies, and competitive response procedures are available through a Knowledge Agent, team members can draw on a shared library of recommendations.

This can standardize workflows, shorten onboarding time for new employees, and reduce reliance on the undocumented knowledge of individual specialists.

6. Scale Gradually

Rather than implementing a comprehensive system across every category at once, organizations can start with a high-volume category and two core agents. As the business value becomes clearer, additional specialists can be introduced.

This incremental approach allows teams to validate results within a limited use case before expanding the solution.

The AWS Technology Stack

The architecture described in the workshop uses the following services and technologies:

• Amazon Bedrock AgentCore. Provides capabilities for deploying and scaling AI agents and supporting communication between them.
• Amazon Ads MCP Server and Amazon Advertising API. Provide integration points for retrieving advertising data and performing campaign operations within the permissions granted.
• Agent2Agent (A2A) protocol. Supports agent discovery and interaction through published capability descriptions.
• Amazon S3 Vectors. Supports semantic search across advertising playbooks, strategy documents, and other knowledge resources. The described solution uses Amazon Titan Text Embeddings V2 to generate vector representations.
• Amazon Bedrock AgentCore Memory. Helps maintain conversational context across multiple steps in a workflow.
• Amazon RDS. Stores historical data used for campaign trend analysis and reporting.

Together, these components can bring conversational interaction, knowledge retrieval, analytics, and operational execution into a unified workflow.

Getting Started With Multi-Agent AI

Amazon Ads provides a hands-on workshop for exploring how to build multi-agent solutions on AWS. The full workshop takes approximately three hours, while a fast-track version takes around one hour.

The workshop introduces an approach to building systems in which specialized agents collaborate to support advertising campaign management tasks.

Access the Amazon Ads hands-on workshop

Conclusion

As advertising portfolios grow, managing campaigns manually becomes increasingly complex. Multi-agent AI on AWS offers an approach that brings analytics, recommendation retrieval, and campaign operations into a unified workflow.

For CPG and retail companies, this can help reduce repetitive work, standardize the application of advertising strategies, and accelerate responses to market changes. Marketing specialists can retain control over business decisions, permissions, and approval processes.

As an official Amazon Web Services partner, Softprom helps organizations explore AWS capabilities and assess cloud technologies for their business needs. The choice of architecture and implementation scenarios should be based on business requirements, security considerations, and measurable expected outcomes.