The rise of agentic AI part 3: Amazon Bedrock Agents monitoring and how observability optimizes AI agents at scale

The next big wave in artificial intelligence is agentic AI, which harnesses autonomous agents to perform tasks by reasoning, learning, and adapting to changing circumstances. The success and efficiency of agentic AI systems depend on how well these AI agents communicate. Facilitating this communication requires monitoring AI agents and their underlying communication protocols, such as Model Context Protocol (MCP).

Part 1 of this blog series, The rise of agentic AI part 1: Understanding MCP, A2A, and the future of automation, covers the fundamentals of AI agents, models, and emerging communication standards like Agent2Agent (A2A) and MCP. Part 2 explores how monitoring A2A and MCP communications results in better, more effective agentic AI. This blog post covers AI agent observability and monitoring, and how to scale and monitor Amazon Bedrock Agents. Full-stack observability for AI with NVIDIA Blackwell and NVIDIA NIM is covered in part 4. Together, these capabilities make it possible to achieve robust, scalable observability in agentic AI environments so teams can build reliable and trustworthy applications and services.

Key takeaways

• Effective cross-agent communication requires standardized telemetry. For foundational model-building platforms like Amazon Bedrock, OpenTelemetry-based solutions provide standardization and instrumentation for tracing and logging to debug at scale.
• End-to end observability is a key best practice for monitoring agentic AI. AI agent observability best practices include using GenAI semantic conventions with traditional logs, traces, and instrumentation.
• Observability helps deliver effective agentic AI results in the context of the whole stack. AI agent observability and Amazon Bedrock Agents monitoring help deliver better performance, ensure compliance, and provide detailed debugging tools.

Cross-agent communication requires standardized telemetry

Given the non-deterministic nature of large language models (LLMs) and dynamic cross-agent communication, organizations need standardized telemetry. OpenTelemetry-based GenAI semantic convention libraries are emerging to unify logging, metrics, and tracing in multi-agent ecosystems. Likewise, these standardized instrumentation libraries let you collect and analyze data from each step in an agent’s decision or communication chain on Dynatrace. Observability of each step lets you monitor the communications among your agents and evaluate their health and performance, regulatory compliance, and debugging.

Scale and monitor Amazon Bedrock Agents with Dynatrace

Amazon Bedrock Agents provide an easy way to build and scale generative AI applications with foundation models.

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models from leading AI companies, such as AI21 Labs, Anthropic, Cohere, Luma, Meta, Mistral AI, poolside, and Stability AI—or from Amazon’s own model, Amazon Nova—all through a single API. In addition, Amazon Bedrock Agents also provide the broad set of capabilities teams need to build generative AI applications with security, privacy, and responsible AI best practices.

Dynatrace provides an AI-powered, unified observability and security solution for tracking and revealing the full context of used technologies and service interaction topology. Using Dynatrace for AI agent monitoring and MCP monitoring, teams can analyze security vulnerabilities and observe metrics, traces, logs, and business events in real time—automatically and securely.

“With the rise of agents, the need for deep visibility and real-time insights is more essential than ever. Through this partnership, AWS and Dynatrace are uniquely positioned to deliver performance, cost, and quality insights alongside robust compliance monitoring—empowering customers to innovate with confidence.”
– Atul Deo, Director of Amazon Bedrock

Best practices for Agent-to-Agent (A2A) and MCP monitoring

As with hybrid and cloud-based environments, context-based observability of AI agents and models is essential for efficient and healthy outcomes. Here are some best practices:

• Adopt common semantic conventions. Standardize metrics and trace attributes—for example, gen_ai.agent.operation.name and gen_ai.agent.name—across different frameworks.
• Use logs and traces for Amazon Bedrock Agents. Log critical task lifecycle events—capability discovery, artifact creation, agent collaboration steps, API calls—so teams can replay and debug complex interactions and detect hallucinations.
• Instrument thoroughly. Bake observability into agent frameworks using external OpenTelemetry libraries or by manually instrumenting calls. Ensure each agent’s start, stop, and reasoning steps, like tools, knowledge base, and guardrails, are captured consistently.
• Secure communication. Enforce enterprise-grade authentication and authorization within agent-to-agent traffic. Use well-defined protocols like A2A to avoid unauthorized data exposure.
• Continuous feedback. Feed observability insights into iterative retraining or fine-tuning for improved agent reliability.

With Amazon Bedrock and the Dynatrace AI Observability solution, you can cover the following use cases for agent observability:

Monitor AI agent service health and performance

• Detect bottlenecks by tracking real-time metrics, including request counts, durations, and error rates.
• Manage service costs with automated cost calculations for each request.
• Stay on track with service-level objectives (SLOs).

Monitor guardrails to ensure compliance

• Monitor your safeguards customized to application requirements and responsible AI policies.
• Validate toxicity, filtered content, and denied topics to ensure compliance.
• Prevent leaks of personally identifiable information (PII).
• Prevent quality degradation by validating models and usage patterns in real time.

End-to-end tracing and debugging

• Achieve complete visibility of prompt flows, from initial request to final response, for faster root cause analysis.
• Capture detailed debug data to troubleshoot issues in complex pipelines.
• Streamline workflows with granular tracing of LLM prompts, including response latency and model-level metrics.
• Resolve issues more quickly by pinpointing exact problem areas in prompts, tokens, or system integrations.

Future of AI agent observability and Amazon Bedrock Agents monitoring

We expect to see deeper integrations between agent orchestration protocols (A2A, MCP) and open observability frameworks, delivering end-to-end visibility from data ingestion to cross-agent collaboration. As standards converge, organizations will rapidly compose advanced AI solutions while retaining full transparency and control, paving the way for even greater scalability, resilience, and confidence in autonomous agents.

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