✍️ 필사 모드: OpenAI, Azure, and AWS: An Enterprise Agent Observability and Evals Comparison Guide
English- At-a-glance comparison
- What each platform is really optimizing for
- What platform, product, and infra teams need
- Rollout decision guide
- Practical checklist
- Official links
As of 2026-04-12, the real enterprise question is not whether agents need observability. They do. The question is where traces live, where evaluations run, and which platform should own rollout decisions.
At-a-glance comparison
| Platform | Traces | Evals | Dashboards | Telemetry integration | Best fit |
|---|---|---|---|---|---|
| OpenAI | Integrated observability to trace and inspect agent workflow execution | AgentKit added datasets, trace grading, automated prompt optimization, and third-party model support | Built into the agent development and optimization loop | OpenAI-native agent stack | Teams that want the shortest path from experiment to improvement |
| Azure | Application Insights and OpenTelemetry-based tracing | Foundry ties evaluation into the build-test-deploy-monitor lifecycle | Agent monitoring dashboard and Foundry observability views | OTEL, Application Insights, Azure Monitor | Microsoft-first enterprise teams that want governance and lifecycle control |
| AWS | CloudWatch traces plus AgentCore observability | Operational validation through AgentCore metrics and trace views | Dashboards for session count, latency, duration, token usage, and error rates | OTEL-compatible integrations with CloudWatch | Platform and infra teams standardizing on AWS operations |
What each platform is really optimizing for
OpenAI is optimizing for a tight agent development loop. On March 11, 2025, OpenAI introduced integrated observability to trace and inspect agent workflow execution. On October 6, 2025, AgentKit extended the loop with datasets, trace grading, automated prompt optimization, and third-party model support. That makes OpenAI strongest when the goal is to move quickly from trace to fix to re-evaluate.
Azure Foundry is optimizing for enterprise lifecycle management. The docs describe tracing setup with Application Insights and OpenTelemetry, a dedicated agent monitoring dashboard, and an explicit build-test-deploy-monitor path with evaluation. That matters when a company wants AI observability to behave like the rest of its release process.
AWS AgentCore Observability is optimizing for operational control in CloudWatch. The docs emphasize dashboards plus OTEL-compatible integrations, with traces, session count, latency, duration, token usage, and error rates surfaced for day-to-day operations. That is a strong fit when CloudWatch is already the operational source of truth.
What platform, product, and infra teams need
Platform teams care about integration shape, portability, and the ability to standardize telemetry across frameworks. Azure and AWS both lean heavily on OpenTelemetry, which makes them easier to fold into an existing observability backbone. OpenAI is better when the agent runtime itself is the product surface and the team wants a first-party trace and eval loop.
Product teams care about iteration speed and evaluation fidelity. OpenAI stands out here because trace grading and automated prompt optimization sit right next to agent development. Azure is also strong because Foundry makes evaluation part of the same lifecycle used to ship and monitor. AWS is more ops-centric, but it still gives product teams the signals they need to decide whether a rollout is healthy.
Infra teams care about telemetry volume, dashboarding, and rollout gates. AWS is the clearest fit if the team already runs on CloudWatch and wants session, latency, duration, token usage, and error metrics in one place. Azure is the best fit when Application Insights is already the enterprise telemetry layer. OpenAI works best when the agent stack is mostly OpenAI-native and infrastructure wants the simplest trace-to-eval feedback loop.
Rollout decision guide
- Choose OpenAI when the agent is built on OpenAI APIs and you want integrated observability plus eval-driven prompt improvement.
- Choose Azure when you want a managed Foundry lifecycle with Application Insights and OpenTelemetry already in the plan.
- Choose AWS when CloudWatch is your operational home and you want OTEL-compatible agent telemetry without adding a separate observability system.
- Use the same rollout gates everywhere: trace coverage, eval repeatability, dashboard usability, and a clear go or no-go threshold before production expansion.
Practical checklist
- Confirm that traces include tool calls, model calls, and error paths.
- Make sure eval datasets reflect production traffic, not just synthetic demos.
- Verify that dashboards answer the questions operators actually ask.
- Keep OpenTelemetry or existing telemetry exports intact to avoid a parallel observability stack.
- Compare the same quality gates before and after rollout.
Official links
- OpenAI agents announcement: New tools for building agents
- OpenAI AgentKit: Introducing AgentKit
- Azure Foundry observability: Observability in Foundry Control Plane
- Azure docs: Observability in Generative AI - Microsoft Foundry
- AWS AgentCore observability: Observe your agent applications on Amazon Bedrock AgentCore Observability
- AWS CloudWatch agent view: Agent view - Amazon CloudWatch
- AWS CloudWatch GenAI observability: Generative AI observability - Amazon CloudWatch
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As of 2026-04-12, the real enterprise question is not whether agents need observability. They do. Th...