Clean trace, wrong output: the visibility gap nobody talks about

Conventional observability is very good at catching execution failure. Across exceptions, timeouts, non-2xx responses, queue backpressure, and memory pressure, most of the instrumentation stack is optimized for cases where one thing (a component, dependency, or resource) crosses a failure boundary, and something else needs to notice.

Agent workflows fail differently. The execution can cheerfully chug along with flawed reasoning or bad retrieval, with no exception being thrown anywhere. The model produced fluent, well-formatted output that happens to be incorrect, irrelevant, or grounded in the wrong context.

This challenge can only be met if you think more broadly about what the system is expected to prove.

What a clean trace can hide

Though the AI agent observability space is extremely new, most teams building agent systems have seen at least one of these scenarios:

• Interpretation mismatch. An agent interprets the task one way at step two and a subtly different way at step five. Each step executes cleanly, but the final output answers a question the user didn't ask.
• Silent tool misuse. An agent calls the right tool with subtly wrong arguments (i.e., passing the wrong date range to the correct API endpoint). Thereafter, the tool dutifully returns 200 while churning out garbage downstream.
• Wrong context retrieved. A RAG pipeline pulls documents that look semantically related but are actually stale. Retrieval span succeeds, generation span succeeds, and the answer is confidently wrong.
• Missing span. The agent skipped a step that it was supposed to take. Nothing failed because there was nothing running, and the resulting absence can only be found if someone realizes it needs to be searched for.

The last one is worth contemplating. Most observability tooling treats a missing span as a no-signal on the premise that, if it isn't there, there's nothing to show. But in an agent workflow, a missing span can be a really important signal, indicating that instrumentation is broken somewhere in the chain, a component failed to fire, or the system made a decision not to do something (which can include things it should’ve done, like hitting a database or fetching a particular document).

Execution signals vs. output signals

There are many different important signals in AI agent systems, but the two we’ll focus on here are:

• Execution signals: Latency, error rate, throughput, resource usage, span structure — tell you whether the system ran. They are what conventional observability captures, and they remain necessary.
• Output signals: Generation quality, grounding, coherence, task completion, semantic correctness — tell you whether the system produced a useful, grounded, or appropriate result. They are what a core part of AI agent observability requires, and they are the cost, quality, and safety signals that conventional observability was never built to capture.

Execution signals are necessary and not sufficient. A clean trace is evidence that the workflow executed, but it is no longer evidence that the answer is correct. Treating the first as a proxy for the second is a category error at the heart of the visibility gap.

What it takes to close the gap

Closing the gap requires instrumentation that captures sufficient context from generation-level artifacts, which could include inputs and outputs at every hop, the complete retrieved context, tool arguments and tool returns, intermediate decisions and the state that produced them, etc. On top of that, you need evaluation signals so that output quality becomes a first-class metric alongside latency and error rate.

This is not a replacement for conventional observability; it’s a powerful new layer being added to it. Execution signals still matter, but execution signals on their own cannot tell you whether the output was correct. The instrumentation has to reach into the reasoning layer, or the reasoning layer is unobservable by definition.

The question that matters

Agent workflows require new instrumentation, new signals, and a fresh mental model for what "working correctly" means for powerful, dynamic, non-deterministic systems. We go deeper on the failure modes that produce clean traces — and how to catch them before they reach users — in our upcoming webinar, “Navigating the Multi-Agent Trap”. We’re hosting it on May 7, 2026, at 11:00 AM PT / 2:00 PM ET. We hope to see you there!