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Why Traditional Observability Breaks with AI Agents

DEV Community·Meidi Airouche·22 days ago
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#genai#aws#ai#fullscreen#reasoning#tool
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One of the biggest mistakes teams make with AI agents is applying traditional observability patterns to non-deterministic systems. A classic backend request is relatively stable: Request → Service → Database → Response Enter fullscreen mode Exit fullscreen mode An agent execution is not. Request ↓ Planning ↓ Memory Retrieval ↓ Tool Calls ↓ Validation ↓ Retries ↓ Response Enter fullscreen mode Exit fullscreen mode Two identical prompts may generate completely different execution paths. That changes observability entirely. The real challenge is no longer monitoring infrastructure. It’s understanding reasoning. This is where AWS AgentCore becomes interesting. Not as another framework. But as a runtime layer for operating probabilistic systems. What You Must Observe in an Agentic Platform Most teams only track: latency token usage request count That is not enough. You need reasoning-level telemetry.…

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