Best Practices for Building Production-Ready AI Agents with Amazon Bedrock AgentCore

Essential Strategies for Developing High-Performance AI Agents in Enterprise Settings

This heading encapsulates the central theme of the document, highlighting the focus on best practices and the specific platform being discussed.

Building Production-Ready AI Agents with Amazon Bedrock AgentCore: Best Practices

Creating a production-ready AI agent involves meticulous planning and execution across the entire development lifecycle. The key differentiator between a captivating prototype and a robust operational AI agent lies in disciplined engineering practices, a resilient architecture, and a commitment to continuous improvement.

In this post, we’ll explore nine essential best practices for designing AI agents utilizing Amazon Bedrock AgentCore—an agent-focused platform that empowers you to create, deploy, and manage AI agents at scale. We’ll discuss everything from initial scoping to organizational scaling, providing you with actionable insights that you can implement immediately.

Start Small and Define Success Clearly

Before diving into development, frame your project around a specific question: “What problem are we solving?” Teams often err by trying to build an all-encompassing agent, leading to unnecessary complexity and slow iteration.

Instead, focus on a distinct use case. For example, if you’re creating a financial assistant, hone in on the three most common tasks analysts perform. If you’re developing an HR helper, target the five most frequent employee inquiries. Ensure these core functionalities are reliable before expanding.

Your initial planning should yield four concrete deliverables:

• Clear Agent Definition: Specify what the agent can and cannot do, share this with stakeholders, and use it to mitigate feature creep.
• Agent Tone & Personality: Decide on the agent’s formality, greeting style, and responses to off-topic questions.
• Unambiguous Tool Definitions: Ensure your tools, parameters, and data sources are well-defined to prevent incorrect choices.
• Ground Truth Dataset: Create a dataset with expected interactions, capturing both typical queries and edge cases.

Example: Financial Analytics Agent

• Capabilities: Retrieve quarterly revenue data, calculate growth metrics, and generate summaries.
• Restrictions: Cannot provide investment advice or access confidential data.
• Tone: Professional but conversational, with transparency about data limitations.

Instrument Everything from Day One

Neglecting observability is a common pitfall. Semantic knowledge about your agent’s performance must be integrated from the start. Amazon Bedrock AgentCore automatically emits OpenTelemetry traces for model invocations, tool calls, and reasoning steps.

Your observability strategy should encompass three layers:

1. Trace-Level Debugging: Enable debugging to visualize each conversation step and swiftly address user-reported issues.
2. Production Monitoring Dashboards: Leverage Amazon CloudWatch Generative AI observability dashboards for real-time monitoring.
3. Analytics on Token Usage and Latency: Use insights to track performance metrics, and export data where needed.

Build a Deliberate Tooling Strategy

Tools connect your agent to the real world. The quality of your tool definitions directly affects agent performance. Clear documentation is essential; avoid ambiguous descriptions.

For instance:

• Poor Definition: “Gets revenue data.”
• Effective Definition: “Retrieves quarterly revenue data for a specified region and time period in millions of USD, requiring a region code (EMEA, APAC, AMER) and quarter in YYYY-QN format.”

Your tooling strategy should cover four areas:

1. Error Handling and Resilience: Define responses for every failure mode.
2. Reuse through Model Context Protocol (MCP): Use existing MCP servers to streamline integrations.
3. Centralized Tool Catalog: Avoid duplication by maintaining a comprehensive catalog of approved tools.
4. Code Examples: Provide actionable examples alongside your tool definitions.

Automate Evaluation from the Start

Understanding whether your changes improve or degrade your agent’s performance is crucial. Automated evaluation processes can define what “success” looks like for your particular use case, ensuring you remain aligned with business objectives.

When planning your evaluation dataset:

• Include multiple phrasings for common queries.
• Capture edge cases that require either escalation or refusal to answer.
• Align technical metrics (e.g., response latency, token cost) with business metrics (e.g., user satisfaction).

This process should iteratively inform your development cycles, allowing you to quickly adapt your agent based on the evaluation feedback.

Decompose Complexity with Multi-Agent Systems

Agents can struggle with too many responsibilities. The solution is to break your tasks into specialized agents that collaborate. This reduces complexity and improves maintainability.

Create a workflow that allows seamless communication between agents to utilize multi-agent systems efficiently. Use AgentCore Memory for shared context, ensuring that engagement with users remains coherent and efficient.

Scale Securely with Personalization

When shifting from prototype to production, security and user personalization are paramount. With AgentCore Runtime, each interaction is isolated in its microVM to ensure privacy.

For user preferences and context, AgentCore Memory facilitates long-term storage of individualized data, allowing agents to tailor interactions accordingly.

Combine Agents with Deterministic Code

While agents thrive in reasoning over ambiguous inputs, deterministic code can execute calculations and validations efficiently. Employ a hybrid approach where agents handle complex logic and deterministic code manages straightforward processes.

Establish Continuous Testing Practices

Deployment isn’t the end; instead, it marks the beginning of a continuous improvement cycle. Regular testing must be integrated within your workflow, ensuring that as user dynamics change and business logic evolves, your agent remains effective.

Implement automated regression testing and A/B testing to compare the efficacy of varied updates against prior versions, prioritizing continual performance enhancements.

Build Organizational Capability

Your first AI agent is merely the beginning. Successful integration of AI agents requires a shift to organizational thinking. Create a platform team that:

• Standardizes tools and practices.
• Monitors agent performance across teams.
• Fosters collaboration to leverage shared learnings.

Conclusion

Building production-ready AI agents demands more than connecting foundation models to APIs. It necessitates disciplined practices that begin with a clearly defined problem and extend across the entire development lifecycle:

• Start small with a clear problem definition.
• Instrument everything from the outset.
• Strategize your tools effectively.
• Automate evaluations.
• Leverage multi-agent architectures.
• Ensure security and personalization as you scale.
• Harness the synergy of agents and deterministic code.
• Continuously test and iterate.
• Foster a culture of organizational capability.

Amazon Bedrock AgentCore equips you with the necessary services to put these best practices into action. Successful agents transcend demo capabilities, delivering tangible business value through robust execution of foundational principles.

Learn More

To explore how to effectively use Amazon Bedrock AgentCore, dive into our documentation and access hands-on workshops designed to accelerate your journey in developing agentic applications.

About the Authors

Maira Ladeira Tanke is a Tech Lead for Agentic AI at AWS, partnering with enterprise customers to drive innovation in autonomous AI systems.

Kosti Vasilakakis is a Principal PM at AWS, focusing on agentic AI and the development of Bedrock AgentCore services.

Utilize these insights and practices to ensure your AI agents not only impress but also deliver substantial business value.