Production Observability for Spring AI Agents on Amazon Bedrock Without Writing Tracing code

2026-05-21 · Source: HackerNoon · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

The article introduces "spring-ai-agentcore-observability", a Spring Boot starter that addresses critical observability challenges for Spring AI agents running on Amazon Bedrock. It solves problems like invisible per-request token costs, untraceable customer complaints, and PII leaks in logs by integrating OpenTelemetry (OTel) with GenAI semantic conventions. The starter, requiring two dependencies and three properties, automatically instruments Bedrock calls, captures token usage, correlates requests with AWS IDs, and redacts sensitive data like emails, SSNs, and API keys before export. This enables detailed cost analysis, efficient incident response, and enhanced compliance posture, validated against "amazon.nova-lite-v1:0" in "us-east-1".

Key takeaway

For AI Engineers or MLOps teams deploying Spring AI agents on Amazon Bedrock, integrating "spring-ai-agentcore-observability" is crucial for operational visibility and compliance. You should adopt this starter immediately to gain per-model token cost dashboards, trace customer issues with AWS correlation IDs, and ensure PII redaction by default. This proactive step prevents costly debugging, security vulnerabilities, and unanswerable finance queries, allowing you to scale confidently.

Key insights

AI agents require specialized observability beyond standard HTTP services due to unique challenges like token cost, PII, and error classification.

Principles

• OpenTelemetry's GenAI semantic conventions standardize LLM observability attributes.
• PII masking should occur at export-time, preserving in-memory data for debugging.
• Automated instrumentation reduces manual effort and ensures consistent telemetry.

Method

The "spring-ai-agentcore-observability" starter uses an AOP aspect to wrap AgentCore HTTP controllers, enriching OpenTelemetry spans with GenAI attributes and AWS correlation IDs. A PII masker then scrubs sensitive strings before the spans are exported.

In practice

• Implement "spring.ai.agentcore.observability.capture-content=true" to capture prompts/completions.
• Configure "spring.ai.agentcore.observability.masking.custom-regex" for org-specific PII patterns.

Topics

• Spring AI
• Amazon Bedrock
• OpenTelemetry
• GenAI Semantic Conventions
• PII Masking
• Observability

Code references

• vaquarkhan/spring-ai-agentcore-observability

Best for: MLOps Engineer, AI Engineer, AI Architect

Related on AIssential

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• Spring AI SDK for Amazon Bedrock AgentCore is now Generally Available — The Spring AI AgentCore SDK simplifies building and deploying scalable, production-ready AI agents on Amazon Bedrock AgentCore using familiar Spring patterns.
• OpenAI's GPT-5.5 and Codex Reach General Availability on Amazon Bedrock — OpenAI's GPT models and Codex are now on Amazon Bedrock, integrating enterprise-grade governance and shifting the competitive AI cloud landscape.
• Amazon Bedrock AgentCore vs Reality — Building reliable AI agents is primarily a software engineering challenge, not just an AI/prompting one.
• Debugging production agents with Amazon Bedrock AgentCore Observability — Production AI agent failures, often silent, require specialized observability for effective diagnosis and resolution.

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Editorial summary, takeaway, and curation by AIssential. Original article published by HackerNoon.