This guest blog post is by Tohn Furutani, SRE Engineer at NTT DATA.
Over the past year, the conversation around generative AI has shifted from single-shot use cases—such as summarization, Q&A, and chat interfaces—to agentic AI systems that can make decisions based on context, plan multistep actions, invoke tools, and adapt as conditions change. As system integrators that collaborate with enterprise customers, we at NTT DATA have explored this shift by continuously validating agentic workflows in our internal AI testbed, GenAI Tech Hub.
Through this work, we quickly learned that the hardest part of agentic AI is making it dependable in production. Enterprises need more than accuracy and speed: They need security, visibility into system decisions, consistent system behavior, and a path to ongoing improvement.
We set out to develop and validate a reusable approach to meet those requirements, building an operational foundation for agentic AI by combining Amazon Bedrock AgentCore with Datadog LLM Observability. In this blog post, we’ll share the decisions that we made and the implementation insights that we gained along the way.
Why we chose Amazon Bedrock AgentCore: A foundation for agent execution
