AI Agent Observability: Beyond Tool Call Logging

Many teams building AI agent systems are logging tool calls, but this approach misses crucial information. While tool calls show if the system's plumbing works, they don't capture the agent's reasoning process. Here's the thing: Agents can fail silently, even when all metrics appear normal. For example, an agent might correctly call tools and get valid responses, yet still produce the wrong outcome. This failure often occurs in the reasoning step that decides which tool to use and why. What's interesting is that this reasoning step doesn't typically produce a trace. This means teams might miss why an agent incorrectly escalates a low-priority ticket, even when all individual calls succeed. It's like having a dashboard show a 99.8% success rate, while the agent is miscalibrated on specific patterns. The bottom line: Traditional tracing focuses on infrastructure, not the agent's internal decision-making. This means you might be missing the real reasons behind agent failures, even when your metrics look perfect.