Optimizing agent system prompts with Amazon Bedrock AgentCore
1mes
- Publicado
- Recolectado
Más de AWS Machine Learning Blog
Inicia sesión para seguir esta fuente
How Natera designed an agentic AI system for lab ops with Amazon Bedrock AgentCore
Natera built SWARM, an agentic AI system on Amazon Bedrock AgentCore that captures institutional lab-operations knowledge and deploys it at scale. This post walks through the full architecture, from knowledge base construction to deployment and evaluation, delivering about 130x faster first response and 96.2% automated coverage across support tickets.
Four Amazon Bedrock inference choices that affect cost
Your Amazon Bedrock inference bill can keep climbing even after you tune prompts and application logic. Four request-level choices (cache duration, service tier, reasoning effort, and maxTokens) change how many tokens the service processes and the rate applied. We show how we tested each and how to verify the effect.
Tokenomics at scale: Track and cap Amazon Bedrock spend per IAM principal with CUR 2.0 and AWS Budgets
Get per-identity Amazon Bedrock cost visibility and enforceable spend limits using only AWS-native features, with no application code for the common case. Attribute spend with IAM principal tags, report with CUR 2.0 or Cost Explorer, and enforce with AWS Budgets actions that apply a Deny policy when a threshold is crossed.
Optimizing cost per task and proving agent ROI on Amazon Bedrock
Comparing per-token prices hides the real cost of running a model on Amazon Bedrock. This post shows how to calculate the true cost per task across four hidden cost drivers, reduce it with caching, routing, and output constraints, and prove agent ROI with human baselines, holdout experiments, and a five-dimension scorecard.