Google Adds Event-Driven Webhooks to the Gemini API, Eliminating the Need for Polling in Long-Running AI — 2026-05-05

## Short Segments Amazon Bedrock AgentCore Identity enhances AI agent security on Amazon ECS, ensuring safe access to external services. Today, we'll explore how Amazon's new identity management service secures AI agents, how Amazon Bedrock uses AI to protect business communications, and why momentum is key to fixing gradient descent's zigzagging. Later, we'll dive into Google's new event-driven webhooks for the Gemini API, which eliminate the need for polling in long-running AI jobs. Amazon Bedrock AgentCore Identity secures AI agents on Amazon ECS. Amazon has introduced Bedrock AgentCore Identity, a standalone service that secures AI agents' access to external services on platforms like Amazon ECS, EKS, and AWS Lambda. This service implements the Authorization Code Grant with secure session binding and scoped tokens, preventing CSRF and browser-swapping attacks. By using OAuth 2.0 and OpenID Connect, it ensures that AI agents have secure, user-delegated access to necessary resources. This development is crucial for maintaining security and efficiency in AI agent operations, allowing developers to manage access tokens and session bindings effectively. Amazon Bedrock uses AI to protect business communications. Amazon Bedrock is leveraging AI to safeguard messaging systems in brokerage businesses, preventing revenue loss and reputational damage from direct buyer-seller communications. By using Amazon Nova Foundation Models, Bedrock can identify attempts at direct contact and provide insights into customer sentiment and service improvements. This approach helps maintain the brokerage's role as a trusted intermediary, protecting commission revenue and partner relationships. For businesses relying on secure communications, this AI-driven solution offers a way to enhance both protection and operational insights. Momentum fixes gradient descent's zigzagging inefficiency. Gradient descent often struggles with uneven loss surfaces, leading to inefficient zigzagging. Momentum addresses this by using past gradients to maintain a running average, allowing faster movement across flat regions and reducing instability. This method improves convergence rates, as demonstrated in a controlled simulation where momentum outperformed standard gradient descent. For developers and researchers, understanding and applying momentum can significantly enhance the efficiency of machine learning models. ## Feature Story Google's event-driven webhooks for the Gemini API eliminate polling in long-running AI jobs. For developers managing production AI pipelines, polling has been a persistent issue, adding latency and consuming resources. Google's new event-driven webhooks provide a push-based notification system, allowing the Gemini API to notify servers in real-time when tasks are complete. This change is significant for agentic and high-volume AI workflows, such as Deep Research and long video generation, where operations can take hours. Previously, developers had to rely on continuous polling, which was both costly and inefficient. With webhooks, the Gemini API can now push a real-time HTTP POST payload to a server endpoint, reducing latency and overhead. This development aligns with Google's shift towards more agentic workflows and high-volume processing, addressing a core pain point in AI job management. For developers, this means more efficient and reliable AI operations, with reduced compute costs and faster response times. As AI applications continue to grow in complexity and scale, such innovations in API management are crucial for maintaining performance and reliability. Looking ahead, the adoption of event-driven webhooks could become a standard practice in AI development, setting a new benchmark for efficiency in long-running operations.