Instrumenting Agents for Observability: What to Log Before You Scale Past Ten

You can run two or three agents on intuition. You cannot run fifty, and the transition sneaks up on you. The teams that scale agents successfully build observability in from agent number one, because an agent you cannot observe is an agent you can neither debug nor trust nor govern, and retrofitting instrumentation after you already have sprawl is painful and incomplete. This is the operational discipline that turns a pile of agents into a system you can actually run.

What "runtime observability" means for agents. Traditional logging captures what code did. Agent observability has to capture something harder: how the agent reasoned. ServiceNow's AI Control Tower, strengthened by the Traceloop acquisition, is built around exactly this, runtime visibility into how agents reason, where they make decisions, and when they need to course-correct, across systems. The reason this is a distinct capability is that an agent's failures are usually not crashes; they are bad decisions that executed perfectly. To catch those, you have to see the decision, not just the outcome.

The minimum you should log per agent run. For every agent invocation, capture: what triggered it and the input context it received; the reasoning or decision path it took; which tools or skills it selected and with what parameters; what each tool returned; the final action or output; and a confidence or outcome signal. With that trace, you can answer "why did the agent do that," which is the question you will be asking constantly. Without it, a misbehaving agent is a black box and your only debugging tool is guessing.

Trace tool and skill selection especially. Most agent failures are selection failures: the agent chose the wrong tool, called it with the wrong parameters, or looped between tools. So log selection explicitly, what the agent chose, what it considered, why. When an agent goes wrong, this is almost always where you find the cause, and it usually points you back to a tool description (T5) that needs sharpening. In multi-agent designs (T1), also log the handoffs, because the failure often lives in the context passed between agents rather than in any single agent.

Connect to AI Control Tower for cross-system visibility. Per-agent logs are necessary but not sufficient once you have many agents, possibly spanning systems via MCP (T6). AI Control Tower is designed to discover, observe, govern, and measure AI deployed across any system, which is what you need when your agents no longer fit in one person's head. Use it to maintain the inventory of what is running, to see behavior in aggregate, and to spot patterns no single agent's log would reveal. The shift is from debugging one agent to operating a fleet.

Set alerts for drift and anomalies. Observability that you only consult after a complaint is reactive. Define what normal looks like for each agent, typical volume, typical tool mix, typical resolution rate, and alert when behavior drifts: a sudden spike in escalations, a tool being called far more than usual, resolution rate falling. Drift is how agents fail slowly: conditions change, the agent keeps acting on yesterday's assumptions, and because it is silent, nobody notices until the damage is visible. Alerts turn that silent drift into a signal you catch early.

Use the logs to tune, not just to firefight. The highest return on observability is not incident response; it is improvement. Mine the traces for where agents hesitate, mis-select, or escalate, and feed that back into better tool descriptions, tighter guardrails, and better grounding. Your observability data is the richest source of "how do I make this agent better" you will ever have. Teams that only look at logs when something breaks are leaving most of the value on the table.

Build this in early. The cost of instrumenting from agent one is small; the cost of trying to add observability after you have fifty unobserved agents in production is a project you will not want to staff. Observe first, scale second.