Microservices architecture is evolving. AI agents are reshaping how backend systems are built and consumed. Discover Agentic Microservices, a new evolution of microservices architecture, along with emerging patterns such as Microservices as Tools (MAT) and Agentic Monoliths. We also explore why serverless is a natural fit for agentic architectures and watch an agentic microservice in action.
The era when organizations relied solely on skilled programmers to write countless lines of code is fading. Developers who once enjoyed crafting well-structured and well-documented code in their preferred programming languages are now openly turning to their Generative AI coding partners to generate code. Here, language is no longer a barrier.
Discover how to use autonomous AI pentesters to test infrastructure security at scale, covering safe deployment, security controls, agent limitations, stopping conditions, and the operational challenges of AI-driven security testing.
This session explores how teams can modernise legacy applications faster using AWS Kiro and AI-powered agentic engineering. By combining specification-driven development with AI engineering agents, the talk will demonstrate how organisations can accelerate delivery, improve software quality and reduce the risks associated with complex transformation programmes.
A practical guide to building secure, scalable, and high-performance multi-tenant APIs using AWS serverless architecture, covering tenant routing, authentication, data isolation, quotas, and observability.
An overview of how AWS data services can provide AI agents with trusted, company-specific knowledge from databases and data lakes, while keeping data quality high and controlling token costs.
Exploring how IAG Loyalty separates an AI control plane from an agent harness to enable governed, scalable, and observable GenAI adoption across the enterprise.
How the AI-Driven Development Lifecycle can help teams build production-ready software faster without compromising quality, using structured AI workflows and a real-world case study.
An insight into G-P’s AI Development Life Cycle, showing how a people-first operating model combined with multi-agent workflows can help teams adopt AI at scale while maintaining engineering rigour and quality.
A hands-on demonstration of building an MCP server with Spring Boot and Spring AI, showing how to securely connect AI models to local tools, databases, APIs, and backend infrastructure.
Learn how to build and operate event-driven architectures, covering observability, testing, schema evolution, error handling, integration, idempotence, documentation, and governance.
A real-world case study of building a production-ready dog training platform in just five weeks using AWS serverless, infrastructure as code, and AI, exploring rapid delivery, architectural trade-offs, and scalability.
An AWS enterprise customer’s journey to securely scaling Agentic AI on AWS, covering enablement, adoption patterns, guardrails, reporting, and logging using Amazon Bedrock and Bedrock AgentCore.
A practical look at using AWS S3 Vectors, Annotations, and Metadata to help AI agents turn unstructured files into searchable compliance data and answer audit questions accurately.
A real-world look at orchestrating multi-agent AI workflows with AWS Lambda Durable Functions, showing how to build resilient, long-running chat experiences with Amazon Bedrock agents and asynchronous human interactions.
Microservices architecture is evolving. AI agents are reshaping how backend systems are built and consumed. Discover Agentic Microservices, a new evolution of microservices architecture, along with emerging patterns such as Microservices as Tools (MAT) and Agentic Monoliths. We also explore why serverless is a natural fit for agentic architectures and watch an agentic microservice in action.
