Improving Agent Performance with Amazon Bedrock AgentCore

Overview

The leap from building a functional AI agent to building a high-performing one is often the steepest part of the development curve. While creating an agent in Amazon Bedrock AgentCore is straightforward, ensuring that it remains accurate, helpful, and efficient requires constant iteration.

With the introduction of Amazon Bedrock AgentCore Optimizations (Preview), AWS has provided a scientific framework for improving agentic applications. This suite of tools, centered on automated recommendations and A/B testing, allows developers to move away from gut-feeling adjustments and toward data-driven excellence.

Introduction

Amazon Bedrock AgentCore Optimizations is a feature set designed to refine the performance of autonomous agents by systematically improving their core configurations. It focuses on two primary workflows:

1. Recommendations: Using historical trace data and sophisticated evaluators to suggest better system prompts and tool descriptions.
2. A/B Testing: Validating those improvements by splitting live traffic between a “Control” (your current version) and a “Variant” (the optimized version) to see which performs better in the real world.

By integrating these features, Bedrock ensures that your agents don’t just work, they evolve.

In the world of Generative AI, small changes can have massive impacts. A single word added to a system prompt or a slight tweak to a tool’s description can be the difference between an agent completing a task or getting stuck in an infinite loop.

Previously, developers had to manually test these changes, often relying on anecdotal evidence or small, manual sample sizes. This was time-consuming and prone to human error. Amazon AgentCore Optimizations automates this lifecycle. It analyzes how your agent is currently behaving, identifies where it is failing (using metrics like “Tool Selection Accuracy” or “Faithfulness”), and suggests concrete improvements. It then provides the infrastructure to safely test those suggestions against your production traffic before a full rollout.

Source: AWS

1. The Recommendation Engine: Data-Driven Refinement

The optimization process usually begins with a Recommendation. This feature takes your existing system prompts or tool descriptions and uses them as “code snippets” or “configuration bundles” to generate improved versions.

How Recommendations are Generated?

To create a high-quality recommendation, Amazon AgentCore looks at your Data Source. This typically involves scanning Amazon CloudWatch log groups for traces of previous agent sessions. By looking at where the agent succeeded or failed in the past, the system can understand the “gap” between the current performance and the desired outcome.

Recommendation Types

• System Prompt Optimization: The engine recommends an improved set of instructions for your agent’s runtime. This might involve adding clearer constraints or better defining the agent’s persona.
• Tool Description Optimization: One of the most common reasons agents fail is that they don’t understand when to use a specific tool. This feature recommends clearer, more descriptive language for your Gateway, helping the model make better tool-selection decisions.

2. The Power of Reward Signals: Evaluators

You can’t optimize what you can’t measure. Amazon AgentCore Optimizations uses a wide array of built-in or custom reward signals and evaluators to assess the quality of an agent’s response. These are categorized into several critical buckets:

• Response Quality Metrics: These evaluate the output itself.
  • Correctness & Faithfulness: Is the info accurate and supported by the sources?
  • Helpfulness & Relevance: Does it actually solve the user’s problem?
  • Conciseness & Coherence: Is it easy to read and logically structured?
• Task Completion Metrics:
  • Goal Success Rate: Did the conversation actually meet the user’s end goal?
• Component Level Metrics:
  • Tool Selection Accuracy: Did the agent pick the right tool for the job?
  • Tool Parameter Accuracy: Did it extract the right data from the user’s query to fill into the tool’s inputs?
• Safety Metrics:
  • Harmfulness & Stereotyping: Ensures the agent remains unbiased and safe for public interaction.
• Trajectory Metrics: These advanced metrics examine the order of operations, ensuring the agent follows a specific logical path (Exact Order, In-Order, or Any-Order matches).

3. Validating Improvements with A/B Testing

Once you have a recommendation (a “Variant”), you need to prove it is actually better than your current setup (the “Control”). The A/B Test feature automates this comparison.

Setting Up the Test

1. Define Control and Variant: You specify two configuration bundles. The “Control” is your baseline, and the “Variant” is your candidate for improvement.
2. Traffic Weighting: You can decide exactly how much traffic to send to each version. For instance, you might send 90% of traffic to the stable Control and 10% to the new Variant to minimize risk.
3. Gateway Routing: The Amazon AgentCore Gateway serves as the traffic controller, dynamically routing requests based on the rules you define.

Analyzing Results

During the test, Amazon AgentCore collects evaluator scores for both versions. You can view side-by-side comparisons of “Goal Success Rate” or “Response Quality.” If the Variant consistently outperforms the Control across your chosen metrics, you can deploy the winning configuration bundle to 100% of your traffic with a single click.

4. Integration with Configuration Bundles

Optimizations are deeply tied to Configuration Bundles. This allows for version control similar to how software developers use Git. You can create different branches for your agent’s configuration, run A/B tests on those branches, and merge the high-performing “winning” configurations back into your main production line.

Conclusion

Instead of guessing why an agent failed to select a tool or why its responses were too wordy, developers now have a suite of metrics and an automated engine to guide them toward the most efficient configuration. As agents become more integrated into our daily workflows, these optimization tools will be the foundation for trust and performance.

Drop a query if you have any questions regarding Amazon Bedrock AgentCore Optimizations and we will get back to you quickly.