Deploying Agents: The Easy Way

Deploying AI agents using serverless cloud functions can be a smart choice for various applications. These functions are well-suited for small batch jobs, real-time text processing, and simple workflows where specific events trigger tasks. With serverless architectures, you can focus on the core functionality of your AI agent without worrying about the underlying infrastructure, scaling seamlessly with demand while only incurring costs when functions are actively executed.

Offline or background AI processes are especially well-suited for serverless deployments. These applications do not require immediate user interactions, allowing for slightly higher latencies. Serverless functions can efficiently manage data processing, analysis, and automation tasks.

They integrate smoothly with databases, data warehouses, and other data sources, making them perfect for batch processing, report generation, and data analysis pipelines. By offloading these tasks to cloud functions, you can ensure your backend processes are scalable and cost-effective.

Example Use Cases

• Ecommerce Order Processing: Imagine a scenario where a serverless AI agent handles customer segmentation for an ecommerce platform. Each new order placed on a platform like Shopify can trigger a function that processes customer data, determines segmentation, and then updates personalized marketing strategies. This method allows businesses to dynamically adjust to real-time buying trends without overwhelming their main application servers.
• HubSpot Automation: For marketing automation, integrating AI agents with HubSpot can streamline customer relationship management. Serverless functions can be triggered by webhook events—such as a lead’s activity or a scheduled task—to analyze customer interactions and update CRM records. This makes it easier to keep customer profiles current and enhances targeted campaign management.
• GitHub Actions Trigger for Coding Agents: Developers can leverage serverless functions to trigger coding agents during pull requests or code merges. For example, a GitHub Action could invoke a serverless function that reviews code changes, runs tests, or even suggests improvements. This approach automates part of the code review process and assists in maintaining code quality in real time.
• Klaviyo Webhook for Audience Updates: In email marketing, AI agents deployed on serverless platforms can listen to Klaviyo webhooks to manage audience data. When a customer’s behavior triggers an update, the function can analyze the latest data and adjust segmentation rules or marketing content accordingly. This ensures that marketing strategies are continuously optimized based on real-time insights.
• Intelligent Dashboards in Looker Studio/Power BI: AI agents can power intelligent dashboards in platforms like Looker or Power BI by delivering actionable recommendations based on the latest data. These dashboards can be updated through daily batch jobs using serverless functions or triggered on-demand by specific data events, ensuring that decision-makers always have timely, data-driven insights to guide their strategies.

Serverless ≠ Stateless

One thing to keep in mind is that serverless doesn't mean stateless. Keeping compute functions stateless simply means that each function execution should run independently—without relying on data stored in memory or previous runs. Instead, any required state should be managed externally through databases, caches, or storage services.

Managing agent state in an SQL database allows AI developers to build flexible, scalable, and cost-effective offline AI agents by leveraging serverless cloud functions. Furthermore, a serverless deployment strategy keeps the system modular and easy to manage, facilitating quick adaptations to evolving business requirements.

The deployment template in this playbook will establish a foundation for creating and implementing advanced AI agents that address real business challenges in marketing, CRM, and ecommerce.

The Deployment Code