Strands Agents
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Building a Self-Testing Agentic AI System
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An advanced red-team evaluation harness is developed using Strands Agents to test the resilience of tool-using AI systems against prompt-injection and tool-misuse attacks. The system orchestrates multiple agents to generate adversarial prompts, execute them against a guarded target agent, and evaluate responses using structured criteria. This approach ensures a comprehensive and repeatable safety evaluation by capturing tool usage, detecting secret leaks, and scoring refusal quality. By integrating these evaluations into a structured report, the framework highlights systemic weaknesses and guides design improvements, demonstrating the potential of agentic AI systems to maintain safety and robustness under adversarial conditions. This matters because it provides a systematic method for ensuring AI systems remain secure and reliable as they evolve.
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Scalable AI Agents with NeMo, Bedrock, and Strands
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AI's future lies in autonomous agents that can reason, plan, and execute tasks across complex systems, necessitating a shift from prototypes to scalable, secure production-ready agents. Developers face challenges in performance optimization, resource scaling, and security when transitioning to production, often juggling multiple tools. The combination of Strands Agents, Amazon Bedrock AgentCore, and NVIDIA NeMo Agent Toolkit offers a comprehensive solution for designing, orchestrating, and scaling sophisticated multi-agent systems. These tools enable developers to build, evaluate, optimize, and deploy AI agents with integrated observability, agent evaluation, and performance optimization on AWS, providing a streamlined workflow from development to deployment. This matters because it bridges the gap between development and production, enabling more efficient and secure deployment of AI agents in enterprise environments.