This quickstart guide provides a step-by-step walkthrough for building, testing, and deploying AI agents using the Amazon Bedrock AgentCore CLI.
- code-based agents for full orchestration control using frameworks like LangGraph or OpenAI Agents
- managed harness preview for rapid configuration-based deployment.
- code-based agents for full orchestration control using frameworks like LangGraph or OpenAI Agents
- managed harness preview for rapid configuration-based deployment.
AWS has released the general availability of its DevOps Agent, a generative AI assistant designed to automate incident investigation and operational tasks. Built on Amazon Bedrock AgentCore, the tool integrates with observability platforms, code repositories, and CI/CD pipelines to autonomously triage issues and correlate telemetry data. New capabilities include support for investigating applications in Azure and on-premises environments, custom agent skills, and personalized reporting.
Key highlights:
* Autonomous incident investigation triggered by webhooks from sources like CloudWatch or PagerDuty.
* Integration with major tools including Datadog, Grafana, Splunk, GitHub, and GitLab.
* Reported performance improvements of up to 75% lower MTTR during preview.
* Pricing model based on cumulative time spent on operational tasks per second.
Key highlights:
* Autonomous incident investigation triggered by webhooks from sources like CloudWatch or PagerDuty.
* Integration with major tools including Datadog, Grafana, Splunk, GitHub, and GitLab.
* Reported performance improvements of up to 75% lower MTTR during preview.
* Pricing model based on cumulative time spent on operational tasks per second.
Amazon Bedrock AgentCore provides an enterprise-grade infrastructure for deploying and managing AI agents. It's model-agnostic, supporting models from Amazon Bedrock, Anthropic, Google Gemini, and OpenAI, and integrates with frameworks like Strands, LangGraph, and CrewAI. Core services include a runtime, memory (short and long-term), a gateway, identity management, a code interpreter, a browser, observability, an evaluation service, and a policy capability. The article details a customer support agent demo, highlighting both the capabilities and potential issues encountered during setup and execution, like deployment warnings and model behavior with policies.
Amazon S3 Vectors is now generally available with increased scale and production-grade performance capabilities. It offers native support to store and query vector data, potentially reducing costs by up to 90% compared to specialized vector databases.
Replace traditional NLP approaches with prompt engineering and Large Language Models (LLMs) for Jira ticket text classification. A code sample walkthrough.
