GENOPS03-BP02 Enable tracing for agents and RAG workflows - Generative AI Lens
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GENOPS03-BP02 Enable tracing for agents and RAG workflows

Implement comprehensive tracing for generative AI agents and RAG workflows to enhance operational excellence and performance efficiency. This practice offers clear visibility into model decision-making, which helps you identify inefficiencies, optimize performance, and debug efficiently. By adopting tracing, customers achieve more reliable and efficient workflows, which improves model accuracy, speeds up decision-making, and enhances overall system performance. This approach supports continuous improvement while keeping data secure throughout the tracing process.

Desired outcome: After implementing tracing, you have enhanced agent decision-making and RAG workflows.

Benefits of establishing this best practice: Learn from all operational events and metrics - Gain insights from tracing for agents and RAG workflows.

Level of risk exposed if this best practice is not established: High

Implementation guidance

Tracing can be a powerful tool for optimizing the decision-making process of agents and RAG workflows. To improve your agent's performance, tracing provides a detailed view of the agent's step-by-step reasoning process. By examining these steps, you can identify areas where the agent might be making suboptimal decisions, taking unnecessary actions, or taking longer than expected.

To optimize your RAG knowledge base, the structure and content should be refined to provide relevant information to the agent. By examining the inputs and outputs at each step, you can refine your prompt templates to guide the agent towards more effective decision-making. When the agent produces unexpected results, the trace can help you understand why those decisions were made and address the root cause.

Each response from an Amazon Bedrock agent is accompanied by a trace that details the steps being orchestrated by the agent. The trace helps you follow the agent's reasoning process that leads it to the response it gives at that point in the conversation. If you enable the trace, in the InvokeAgent response, each chunk in the stream is accompanied by a trace field that maps to a TracePart object. The TracePart object contains information about the agent and sessions, alongside the agent's reasoning process and results.

To optimize the performance of multiple agents working in parallel using trace data in Amazon Bedrock. To optimize data transfer between agents and reduce latency in your multi-agent system using Amazon Bedrock, consider using the supervisor with routing mode. This mode allows the supervisor agent to route information directly to the appropriate collaborator agent, reducing unnecessary data transfers and overall latency.

Implementation steps

  1. Collect and aggregate trace data.

    • Implement a system to collect trace data from agents involved in your parallel processing workflow

    • After running an Amazon Bedrock Agent, view the trace in real-time as your agent performs orchestration

    • When making an InvokeAgent request to the Amazon Bedrock runtime endpoint, set the enableTrace field to TRUE. This will include a trace field in the InvokeAgent response for each chunk in the stream

    • Store this data in a centralized location, such as Amazon S3 or Amazon CloudWatch Logs, for quick access and analysis

  2. Secure trace data.

    • Implement appropriate access controls to verify that only authorized personnel can view trace data

    • Be mindful of any sensitive information that might be included in traces and handle it according to your organization's security policies

  3. Analyze the trace components.

    • The trace is structured as a JSON object containing fields such as agentId, sessionId, and trace

    • PreProcessingTrace shows how the agent contextualizes and categorizes user input

    • OrchestrationTrace reveals how the agent interprets input, invokes action groups, and queries knowledge bases

    • PostProcessingTrace demonstrates how the agent handles the final output and prepares the response

    • FailureTrace indicates reasons for step failures

    • GuardrailTrace shows actions taken by the Guardrail feature

  4. Analyze runtimes.

    • Review the timestamps in the trace data to identify which agents or steps are taking the longest to complete

    • Look for patterns or bottlenecks that might be causing delays in the overall process

  5. Examine resource utilization.

    • Use the trace data to understand how each agent is utilizing resources such as knowledge bases or action groups

    • Identify overutilization or underutilization of resources that might be affecting performance

  6. Optimize agent configurations.

    • Based on the analysis, adjust the configuration of individual agents to improve their performance

    • This may include fine-tuning prompts, adjusting knowledge base queries, or modifying action group structures

  7. Implement load balancing across agents

    • Use the insights gained from trace data to distribute workloads more evenly across agents

    • Consider implementing a dynamic load balancing system that can adjust based on real-time performance metrics

  8. Optimize data transfer between agents

    • Use the supervisor with routing mode, which allows the supervisor agent to route information directly to the appropriate collaborator agent, reducing unnecessary data transfers and overall latency

    • Use the session state feature to maintain context between agent interactions, reducing the need to transfer redundant information

    • Where possible, design your multi-agent system to process tasks concurrently, reducing overall runtime

    • Where appropriate, cache frequently accessed data to reduce repeated transfers between agents

    • Deploy your agents in AWS Regions closest to your users or data sources to minimize network latency

  9. Optimize your knowledge bases.

    • Verify that each agent's knowledge base is well-structured and contains only relevant information to minimize unnecessary data processing

  10. Set up performance monitoring.

    • Use Amazon CloudWatch to create custom metrics based on the trace data

    • Set up alarms to alert you when performance falls below expected thresholds

  11. Conduct iterative testing.

    • After making optimizations, run comprehensive tests to measure the change in overall system performance

    • Use the trace data from these tests to identify further areas for improvement

  12. Document and share insights.

    • Keep a record of optimizations made and their effects on performance

    • Share these insights with your team to improve future multi-agent system designs

Resources

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