AWS for Industries
Build a Dynamic Pricing Solution for Restaurants using Agentic AI Strands Agents
Introduction
Customer demand that fluctuates throughout the day creates a significant challenge for restaurants. During peak hours, traffic surges can strain kitchen capacity and impact service quality. During off-peak hours, underutilized capacity leads to potential revenue losses of up to 40 percent. Traditional fixed-priced menu models do not address these fluctuations effectively.
This blog post introduces a serverless dynamic pricing platform built on Amazon Web Services (AWS) that helps restaurants automatically adjust menu prices based on real-time demand patterns. The solution enables restaurants to maximize revenue during peak hours while attracting price-sensitive customers during slower periods.
Using agentic AI built with Strands Agents and Amazon Bedrock AgentCore, restaurant pricing can be dynamically adjusted based on the following factors:
- Real-time demand
- Weather conditions
- Local events and holidays
- Competitor pricing
Business challenge
Restaurants typically face several key challenges with traditional pricing approaches:
- Inefficient resource utilization: Staff and kitchen capacity are overwhelmed during peak hours but underutilized during off-peak times
- Missed opportunities: Price-sensitive customers who might visit during slower periods are deterred by standard pricing
- Operational complexity: Manual price adjustments are time-consuming and difficult to implement consistently
- Competitive blind spots: Without visibility into competitor pricing, restaurants cannot position their offerings effectively
A dynamic pricing solution addresses these challenges by automatically analyzing multiple data points and adjusting prices in real time to optimize both revenue and customer satisfaction.
Benefits
Restaurants implementing a dynamic pricing solution can expect:
- Increased revenue: Off-peak traffic increases up to 35 percent while peak hour revenue is maintained
- Improved resource utilization: Customer flow is more balanced throughout operating hours
- Enhanced customer experience: Price-sensitive customers gain access to dining experiences during off-peak times
- Data-driven decision making: Demand patterns and pricing elasticity become better understood
- Greater operational efficiency: Automated pricing adjustments reduce manual work
- Reduction in food wastage: Greater off-peak customer traffic reduces end-of-day waste, achieving cost savings.
- Better competitive positioning: Real-time awareness of competitor pricing ensures optimal market positioning
How AWS helps address dynamic pricing
AWS agentic AI, serverless compute, and managed database services can support and accelerate dynamic pricing for restaurants. Amazon Bedrock AgentCore provides a managed runtime for deploying AI agents built with the Strands Agents SDK. These agents can orchestrate complex decision-making by analyzing multiple data sources simultaneously through Model Context Protocol (MCP) servers.
Amazon Bedrock AgentCore allows you to run sophisticated pricing algorithms with scalability, reliability, and operational simplicity. AgentCore offers a flexible option for adding agentic AI to automate tasks that traditionally require human analysis and decision-making.
Following is a reference solution architecture diagram that depicts how each group of services helps bring intelligence to restaurant pricing.
Architecture
Figure 1: Reference solution architecture for dynamic pricing using Agentic AI Strands Agents
Solution walkthrough
In this section, we walk through each component to show how agentic AI helps restaurants optimize pricing:
1. User interface and triggers: The restaurant’s menu is hosted as a static website on Amazon S3, displaying dynamically updated prices. Amazon EventBridge triggers price recalculation at configurable intervals (typically hourly) via an AWS Lambda function that orchestrates the pricing optimization flow.
2. Amazon Bedrock AgentCore Runtime (Strands Agents): The core intelligence resides in Amazon Bedrock AgentCore Runtime, which hosts multiple Strands Agents in a swarm pattern. The Restaurant Swarm Agent orchestrates specialized child agents and aggregates their findings for the final pricing:
- Demand Agent: Analyzes current pending orders relative to kitchen capacity to understand real-time demand
- Menu Agent: Ingests the restaurant’s standard menu with base prices, ensuring pricing adjustments stay within configured bounds
- Weather Agent: Retrieves weather forecasts to predict customer traffic impact
- Local Events Agent: Identifies local events (sports games, concerts, festivals) that may drive higher orders
- Competitor Pricing Agent: Scans competitor menus and pricing through web search for competitive positioning
3. Amazon Bedrock AgentCore Gateway (MCP servers): Each child agent communicates with its data sources through MCP servers hosted on Amazon Bedrock AgentCore Gateway. MCP servers provide a standardized interface for agents to interact with both internal and external data sources. The gateway includes:
- DynamoDB MCP Server: Reads data from internal Amazon DynamoDB tables containing pending orders and the restaurant’s base menu with pricing
- Weather MCP Server: Calls external weather APIs to retrieve current and forecasted weather conditions
- Local Events MCP Server: Calls external local events APIs to identify upcoming local happenings
- Competitor Pricing MCP Server: Performs web searches to gather competitor pricing data
4. Internal data sources (Amazon DynamoDB): Amazon DynamoDB serves as the primary data store, providing single-digit millisecond performance at scale. Two key tables support the solution:
- Pending Orders: Stores real-time order data from restaurant POS systems, mobile apps, and online ordering platforms, providing transaction data and occupancy information
- Restaurant Base Menu: Contains the standard menu with base prices, item costs, and configured minimum pricing thresholds to ensure profitability
5. External APIs: The solution integrates with external data providers to enrich the pricing decision with real-world context:
- Weather API: Provides current conditions and hourly forecasts
- Local Events API: Provides information about upcoming local events that may impact customer traffic
- Competitor Pricing API (web search): Gathers current pricing from competitor restaurants
6. Pricing decision and output: The Restaurant Swarm Agent aggregates the inputs and applies pricing logic to determine updated menu prices. Key pricing rules include:
- Updated menu prices are never set lower than the configured cost threshold, ensuring profitability. For this solution, minimum prices can be configured as a percentage of base menu prices.
- During peak hours, prices match the base menu pricing.
- During off-peak hours, prices may be reduced to attract price-sensitive customers.
- Competitor pricing data is used to ensure competitive positioning.
- Updated prices are stored in DynamoDB and reflected on the S3-hosted website.
The serverless architecture requires minimum infrastructure. Restaurants can use these services to enhance pricing intelligence and gather value-added data points for revenue optimization. This solution can be applied to hotels, airlines, ride-shares, and other industries as well, where dynamic pricing can drive revenue and customer satisfaction.
Deployment recommendation
For production workloads, we recommend deploying Strands Agents on Amazon Bedrock AgentCore. AgentCore provides:
- Managed runtime for hosting and scaling agents without infrastructure management
- A built-in gateway for MCP server integrations
- Observability with tracing and monitoring of agent interactions
- Session management for maintaining conversation context
- Security with IAM-based authentication and authorization
AgentCore handles the operational complexity of running AI agents at scale, allowing restaurant teams to focus on business logic rather than infrastructure.
Conclusion
This blog explains how agentic AI technology can be leveraged for dynamic pricing automation in the restaurant industry, driving higher revenue, reduced food wastage, and increased customer satisfaction.
The serverless architecture provides flexibility, scalability, and cost-effectiveness, allowing restaurants of all sizes to benefit without significant upfront investment. By deploying on Amazon Bedrock AgentCore, organizations achieve production-ready agent deployments with minimal operational overhead.
Contact your AWS account team to learn how we can help accelerate your business.