Observe agent decisions, not logs

AI agent observability is becoming its own engineering discipline — fast. That was the real signal this week, across Grafana’s product launch, F5’s security pitch, and the broader enterprise governance conversation. The old idea was simple: collect logs, watch latency, maybe inspect a bad response after the fact. But agents don’t fail like normal software. They fail because they choose the wrong tool, take the wrong branch, loop too long, or act on stale context. ### Why aren’t logs enough? Logs tell you what happened. Agents force you to ask why that path happened. A normal service usually follows a predictable route from input to output. An agent doesn’t. The same request can trigger different tool calls, different delegation paths, and different outcomes on different runs, which means point-in-time logs miss the thing you actually need — the execution trajectory. That’s why newer observability work is moving toward traces, spans, and agent-aware telemetry instead of plain application logging. (zylos.ai) ### What changed this week? Grafana used GrafanaCON 2026 to roll out AI observability in public preview inside Grafana Cloud, with the explicit goal of watching agent inputs, outputs, and execution flows in production. The interesting part is not just that Grafana added another dashboard. It’s that a mainstream observability company is now treating a(zylos.ai)ogs, metrics, and traces. That’s a pretty clear sign this has moved from niche tooling into core platform plumbing. (siliconangle.com) ### What are teams actually trying to see? Basically three things. First, system health — latency, token burn, and cost. Second, decision path — which tool the agent picked, what happened at each step, where it branched, and where it got stuck. Third, outcome quality — whether the task actually completed, whet(siliconangle.com)age now, even when they describe it differently. Grafana talks about inputs, outputs, and execution flows. F5 talks about intent, context, and reasoning behind every decision. The common thread is the same: observe behavior at the action layer. (siliconangle.com) ### Where do agents usually break? Turns out, not mainly at the model layer. A lot of failures come from tool-call errors, context truncation, runaway loops, and silent drift after a prompt, model, or schema change. That matters because standard APM was built to catch crashes and slow requests, not a 17-step w(siliconangle.com)wer instead of the real cause. (digitalapplied.com) ### So what does good instrumentation look like? The practical pattern is event-style tracing for every meaningful step — LLM call, tool invocation, memory read, handoff, guardrail check, and final outcome. OpenTelemetry is becoming the portable layer for that, because it gives teams a common vocabulary for agent spans without lockin(digitalapplied.com)ls, frameworks, and observability stacks while trying to get agents stable. (zylos.ai) ### Why is security suddenly part of observability? Because agents act. F5’s point this week was that security controls have to sit at the inference layer — where the agent is interpreting data and making decisions — not just at the network or API edge. If an agent can cross systems, assume a user’s permissions, and trigger business actions, then obs(zylos.ai)ut whether the agent should have made it in the first place. (siliconangle.com) ### What are platform teams doing with that? They’re narrowing the operating surface. Appian and PwC framed this as moving controls out of human review and into the process itself. In practice, that means “golden paths” — tightly defined workflows, explicit permissions, approved tools, and human checkpoints where the blast radiu(siliconangle.com)out of it. (siliconangle.com) ### What’s the bottom line? The shift is from watching outputs to watching decisions. If your agent stack only tells you response time and token count, you’re still blind. The teams that will ship reliable agents are the ones instrumenting every step, scoring outcomes continuously, and treating agent behavior as something to govern in real time — not something to inspect after it breaks. (siliconangle.com)