This post is co-written with Vikram Gundeti and Nate Folkert from Foursquare.
Personalization is key to creating memorable experiences. Whether it’s recommending the perfect movie or suggesting a new restaurant, tailoring suggestions to individual preferences can make all the difference. But when it comes to food and activities, there’s more to consider than just personal taste. Location and weather also play a crucial role in shaping our choices. Imagine planning a day out: on a sunny afternoon, a leisurely picnic in the park might be ideal, but if it’s pouring rain, a cozy indoor café would be much more appealing. The challenge, then, is to create an agent that can seamlessly integrate these factors—location, weather, and personal preferences—to provide truly personalized recommendations.
To tackle this challenge, we can combine Amazon Bedrock Agents and Foursquare APIs. In this post, we demonstrate how you can use a location-aware agent to bring personalized responses to your users.
Amazon Bedrock Agents
Amazon Bedrock that makes it straightforward to build and scale generative AI applications. It provides access to a variety of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Luma, Meta, Mistral AI, Stability AI, and Amazon, all through a single API. This means you don’t need to manage infrastructure, because it’s serverless and integrates with familiar AWS services for security, privacy, and responsible AI. You can experiment with models, customize them with your data, and build applications without writing complex code.
Amazon Bedrock Agents is a feature within Amazon Bedrock that allows you to create autonomous AI agents. These agents can understand user requests, break them into steps, and complete tasks by connecting to your company’s APIs and data sources. For example, they can automate processes like processing insurance claims or managing inventory, making them efficient for business tasks. They handle prompt engineering, memory, and security automatically, so you can set them up quickly without managing infrastructure.
Foursquare Places APIs
Foursquare’s Places APIs deliver precise location intelligence for applications requiring contextual awareness. Built on top of the open source global Places dataset with 100 million points of interest spanning 1,500 categories, the Places APIs transform geographic coordinates into actionable business context.
The GeoTagging API accurately resolves GPS coordinates to a specific place with a high degree of precision, enabling applications to instantly determine if a user is at a local coffee shop, inside a Macy’s department store, or standing in Central Park. The Place Search & Data APIs transform how applications discover locations by providing nuanced filtering capabilities beyond simple proximity searches. Developers can filter places by specific categories (finding only restaurants, parks, or tourist attractions), apply attribute-based constraints (such as price range or special amenities), consider temporal factors (like current operating status), and balance distance with relevance for truly contextual results. Each place returned comes enriched with contextual attributes, including photos, reviews, quality ratings, and real-time popularity data.
When integrated with Amazon Bedrock Agents, Foursquare’s Places APIs enable the creation of applications that understand the complete context of a user’s location—resulting in experiences that are relevant, timely, and personalized.
Solution overview
To demonstrate the power of adding location to Amazon Bedrock Agents, we created a simple architecture that creates an Amazon Bedrock agent with the Foursquare Places APIs and a Weather API. By combing these capabilities, we can create unique user experiences that are customized to the context of where the user is. The following diagram shows how we architected the agent.
In the solution workflow, the user interacts with the agent through a Streamlit web interface. The web application uses the application logic that invokes the Amazon Bedrock agent in the cloud. The agent knows about the location tools and weather tools even though these are hosted locally inside the application. When the tools are invoked by the agent, a return of control response is given to the application logic, which invokes the tool and provides the response from the tool in a second invocation of the agent. In addition to the tools, the agent has basic instructions on what type of personality it should have and what types of behaviors it should support.
Let’s explore an example of a brief interaction with the agent where we ask if there is a park nearby and a recommended restaurant near the park for takeout food.
The following screenshot shows the first interaction with an agent, locating a park nearby with the Foursquare APIs invoked by the agent.
In this example, you can see the agent sending intermediate events to the user informing them of the actions taking place (invoking the model, invoking a tool, thinking, and so on).
The following screenshot shows the list of restaurants recommended by the Foursquare APIs near the park.
In this example, the agent invokes the APIs based on the user input, and the Streamlit UI connects the output from Foursquare to a map.
In the following section, we detail how you can build the agent in your account and get started.
Prerequisites
To deploy this solution, you should have an AWS account with the necessary permissions.
You will also need a Foursquare Service API Key to allow your AI agent to access Foursquare API endpoints. If you do not already have one, follow the instructions on Foursquare Documentation – Manage Your Service API Keys to create one. You will need to log in to your Foursquare developer account or create one if you do not have one (creating a basic account is free and includes starter credit for your project). Be sure to copy the Service API key upon creation as you will not be able to see it again.
Build the agent
The source code for the Foursquare agent is available as open source in the following GitHub repository. Complete the following steps to up the agent in your local folder from the source:
- Clone the repository to a local folder.
- Set environment variables for your Foursquare API token:
export FOURSQUARE_SERVICE_TOKEN=<Foursquare API token>
- Set environment variables for your AWS credentials:
export AWS_ACCESS_KEY_ID=<AWS_ACCESS_KEY_ID>
export AWS_SECRET_ACCESS_KEY=<SECRET_ACCESS_KEY>
- Install requirements:
pip install requirements.txt
- Start the Streamlit UI:
streamlit run agent_ui.py
Best practices
When you’re creating an agent, we recommend starting with a test dataset. Think through the possible inputs and what are acceptable outputs. Use these sample conversations to test the agent whenever a change is made. In addition, Amazon Bedrock Agents allows you to configure guardrails to protect against malicious input or types of conversation that you would not want to use for your user experience. We recommend for any production use cases to couple your agent with appropriate guardrails. To learn more, see Amazon Bedrock Guardrails.
Clean up
When you’re done using the solution, remove any resources you created to avoid ongoing charges.
Conclusion
Agents provide a mechanism to automate work in behalf of your customers, whether through a chat interface or other inputs. Combining the automation possible with agents with the location-aware APIs from Foursquare, you can create powerful UIs and experiences that will delight your customers with new levels of personalization. With Amazon Bedrock Agents, you can build a cloud-centered solution that allows you to use powerful foundation models on Amazon Bedrock to drive these experiences.
Try out the solution for your own use case, and share your feedback in the comments.
About the authors
John Baker is a Principal SDE at AWS, where he works on Amazon Bedrock and specifically Amazon Bedrock Agents. He has been with Amazon for more than 10 years and has worked across AWS, Alexa, and Amazon.com. In his spare time, John enjoys skiing and other outdoor activities throughout the Pacific Northwest.
Mark Roy is a Principal Machine Learning Architect for AWS, helping customers design and build generative AI solutions. His focus since early 2023 has been leading solution architecture efforts for the launch of Amazon Bedrock, the flagship generative AI offering from AWS for builders. Mark’s work covers a wide range of use cases, with a primary interest in generative AI, agents, and scaling ML across the enterprise. He has helped companies in insurance, financial services, media and entertainment, healthcare, utilities, and manufacturing. Prior to joining AWS, Mark was an architect, developer, and technology leader for over 25 years, including 19 years in financial services. Mark holds six AWS Certifications, including the ML Specialty Certification.
Vikram Gundeti currently serves as the Chief Technology Officer (CTO) of Foursquare, where he leads the technical strategy, decision making, and research for the company’s Geospatial Platform. Before joining Foursquare, Vikram held the position of Principal Engineer at Amazon, where he made his mark as a founding engineer on the Amazon Alexa team.
Nate Folkert is a Senior Staff Engineer at Foursquare, where he’s been since spotting it trending nearby when checking in at a Soho coffee shop 14 years ago. He builds the server API for Swarm and helps out on special projects. Outside of work, he loves exploring the world (with Swarm, ofc, so is it really outside of work?) and is currently obsessed with finding all of the irl filming locations used in Apple TV’s Severance