# Beyond the 200 OK: Architecting Observability for AI

DevFeed: [Beyond the 200 OK: Architecting Observability for AI](<https://devfeed.tech/articles/beyond-the-200-ok-architecting-observability-for-ai-12648.md>)

Original publisher: [Read original article](<https://nordicapis.com/beyond-the-200-ok-architecting-observability-for-ai/>)

Author: Adriano Mota

Published: 2026-09-11T07:00:00Z

Content type: article

Language: en

Sources: [Nordic APIs](<https://devfeed.tech/sources/nordic-apis.md>)

Topics: [ai observability](<https://devfeed.tech/topics/ai-observability.md>), [observability](<https://devfeed.tech/topics/observability.md>), [Application Performance Management (APM)](<https://devfeed.tech/topics/apm.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [ai-observability](<https://devfeed.tech/tags/ai-observability.md>), [api-logging](<https://devfeed.tech/tags/api-logging.md>), [api-metrics](<https://devfeed.tech/tags/api-metrics.md>), [api-monitoring](<https://devfeed.tech/tags/api-monitoring.md>), [api-security](<https://devfeed.tech/tags/api-security.md>), [api-testing](<https://devfeed.tech/tags/api-testing.md>), [apm](<https://devfeed.tech/tags/apm.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [blog](<https://devfeed.tech/tags/blog.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [llm](<https://devfeed.tech/tags/llm.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [observability](<https://devfeed.tech/tags/observability.md>)

## AI overview

An article about designing observability for AI systems beyond traditional APM metrics, with emphasis on tracking quality, cost, retrieval, and agent behavior.

## Source excerpt

Traditional monitoring tools, such as application performance monitoring (APM), were engineered to monitor deterministic software where specific inputs reliably lead to predictable outputs through hard-coded logic. When a traditional API fails, it usually throws a 500 Internal Server Error. But when an AI agent fails, it might return a perfectly healthy 200 OK status code ...