𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝗻 𝗔𝗜: 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗶𝘀 𝗡𝗼𝘁 𝗘𝗻𝗼𝘂𝗴𝗵

𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝗻 𝗔𝗜: 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗶𝘀 𝗡𝗼𝘁 𝗘𝗻𝗼𝘂𝗴𝗵

Traditional software is easy to monitor. An API fails. A server crashes. Your dashboard turns red. You see the error. You fix it.

AI systems are different. Your API responds fast. Your CPU usage is low. Your dashboard stays green. But the answer is wrong.

It is a silent failure. System health does not equal decision quality.

Many teams log everything. They log every prompt and response. This is a mistake. It raises costs. It risks privacy. It creates noise.

You need the right signals. Stop asking if the system runs. Start asking if the decision is right.

Track these metrics:

• Is the output correct?
• Does it match user intent?
• Is the response safe?
• Is the cost increasing?
• Is the model drifting?

Build a feedback loop. A wrong answer is not a bug. It is a lesson. Use it to improve prompts.

Use an AI gateway. It acts as a central hub. It helps you track routing and costs.

Key takeaways:

• Do not trust green dashboards.
• Track decision quality.
• Avoid logging everything.
• Build feedback loops.
• Use an AI gateway.

Stop monitoring uptime. Start monitoring trust.

Source: https://dev.to/luke076/observability-in-ai-why-monitoring-systems-is-no-longer-enough-kp5 Optional learning community: https://t.me/GyaanSetuAi