Building Scalable Multi-Agent Red-Teaming Platforms: Design Patterns & Best Practices (The Automation Stack) - Tapa blanda

Building Scalable Multi-Agent Red-Teaming Platforms (Paperback)

Paperback. Condición: new. Paperback. Building scalable red-teaming systems isn't just about generating adversarial prompts-it's about orchestrating a network of intelligent agents that work together securely, efficiently, and at scale. This book shows you how to do exactly that.Whether you're a security engineer probing LLM behavior or a platform architect tasked with automating adversarial testing pipelines, this hands-on guide will walk you through the process of designing, deploying, and scaling a multi-agent red-teaming platform. It's built on real-world implementations using containerized microservices, policy-as-code engines, and cloud-native orchestration.You'll learn how to assign and manage agent roles-adversary, observer, guardian-then connect them via reliable messaging patterns. The book covers policy enforcement, test scheduling, response validation, and continuous improvement pipelines. From fault-tolerant execution and secure communications to canary rollouts and self-optimization, every chapter offers immediately usable examples with working code, configuration templates, and architectural diagrams.What's inside this book?Design patterns for distributed multi-agent red-teaming using open-source toolsRealistic deployment models with Helm, Kubernetes, service mesh, and GitOpsPolicy management strategies using OPA, JSON Schema, and structured logsComplete CI/CD templates for red-team test automationPractical code examples, metrics collection, and rollback safety techniquesIf you're building, improving, or maintaining an AI security pipeline, this book belongs on your desk. Get your copy now and start scaling red-team capabilities with confidence. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9798285646907

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