The Bedrock Guardrails safeguards I use, the ones I skip and why, how I handle interventions and false positives, and what the protection costs on a public agent.

I have traces and evaluations running on my production AI agent. This is what I actually do with them day to day: catch the bad answers by score, turn them into regression tests, and why the next step I still need is an alert.

How I trace my Strands agent on Amazon Bedrock AgentCore with Langfuse, inspect model and tool execution, and turn real production failures into eval cases.

How I test my AI agent beyond unit tests: tool behavior, parameters, LLM-as-a-judge evaluations, and a regression suite built from real failures.

In this article we explore how to effectively manage prompts in Amazon Bedrock, covering key functionalities such as versioning, optimization and collaboration to enhance your AI-based projects.

Discover how to design effective prompts in Amazon Bedrock to improve the quality of AI model responses. Learn how to structure your prompts, apply advanced techniques, and validate their effectiveness.

Discover how Amazon Bedrock simplifies access to AI foundation models, their use cases, advantages, and how to integrate them into applications to generate content, analyze data, and improve processes.

Explore AWS's generative AI offerings, including Amazon Q Developer, Amazon Bedrock, and Amazon SageMaker, through hands-on experiences and insights from AWS re:Invent 2024.