Accelerating Agent Development with Amazon Bedrock AgentCore
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Mitch Lewis
This report provides a summary overview of our testing and analysis of Amazon Bedrock AgentCore, with a full Lab Insight Report report coming soon.
Challenges of Agentic Development
Agentic AI presents enterprises with a significant advancement over basic Large Language Models (LLMs), enabling autonomous applications that can perceive, reason, plan, and execute actions by leveraging functional tools. This capability transforms AI into a practical enterprise automation tool to solve complex, real-world problems. However, developing and deploying these agents involves considerable complexity. Practitioners must select appropriate models and agentic frameworks, while also addressing critical challenges related to security, integration with existing systems, scalability, and the robust monitoring of intricate agent behavior. The lack of standardized, purpose-built development toolkits often forces developers to spend extensive time on infrastructure and custom solutions, ultimately delaying the time-to-production and hindering the realization of agentic AI’s full business value.
Accelerating Agentic Development with Amazon Bedrock AgentCore
Amazon Bedrock AgentCore gives developers a flexible, modular toolkit and a comprehensive agentic platform to rapidly accelerate the development and deployment of AI agents, while providing enterprise grade security and scalability. To evaluate the efficiencies gained by utilizing AgentCore, Signal65 engineers developed three distinct example agents, outlined below:
- Customer Service Agent: A friendly customer service chatbot capable of providing product information and customer assistance.
- Operations and Product Management Agent: An AI agent that monitors and interacts with external project management tools to enhance project management efficiency and escalate issues.
- Market Research Agent: A useful automation agent that conducts market research by browsing specified web domains to collect information and answer specific research questions.
Each agent was built twice: once utilizing Amazon Bedrock AgentCore – a fully managed deployment, and again with an approach assembled from separate tools and services, referred to as a “custom deployment”, requiring customers to use several disconnected tool sets and invest significant manual effort. All development time was recorded for both approaches, ultimately finding AgentCore to be significantly more efficient:
