Monitoring AI Agents: Observability, Logs & Run Auditing

Monitoring an AI agent means being able to answer, at any moment: what is it doing, what has it done, and is it staying inside its limits. Deelo answers all three with a durable run for every agent execution -- a crash-safe record that moves through defined states -- plus a runs API and activity log for the full audit trail, report-back summaries so the agent tells you what it did, and usage caps that stop a runaway before it costs you. Observability isn't a nice-to-have you add later; it's the thing that makes autonomy safe enough to grant at all.

Here's the counterintuitive part: monitoring isn't the thing you set up after the agent's running. It's the thing that lets you grant autonomy in the first place. The reason you can promote an agent from assisted to semi-autonomous is that you watched a week of its runs and saw it make good decisions. No visibility, no evidence; no evidence, no trust; no trust, no autonomy. Observability is the currency you buy autonomy with.

That reframes what monitoring is for. It's not only about catching the agent doing something wrong after the fact -- though it does that too. It's the feedback loop that tells you when to give the agent more rope and when to pull it back. Set it up first, and every other deployment decision gets a data source.

The unit of monitoring in Deelo is the run: one durable record for every time an agent executes. 'Durable' is the load-bearing word. The run is crash-safe -- if a server restarts mid-execution, the run isn't lost or silently re-started from scratch; it resumes from a known state. And tool calls are dispatched exactly once, so a hiccup can't cause the agent to send the same email twice or double-charge a customer. For anything an agent does that touches the real world, 'exactly once' is the difference between a reliable system and a liability.

Every run carries its own history: what the agent was asked, the steps it took, the tools it called, what it changed, where it paused, and how it ended. That record is what you inspect, audit, and learn from.

A run moves through a small set of states, and knowing which one a run is in tells you what's happening without reading a transcript:

A queue of runs stuck in 'waiting for approval' tells you your approval workflow is the bottleneck. A cluster of 'failed' runs points at a tool or a scope problem. The states turn a pile of activity into a diagnosis.

Two surfaces give you the audit trail. The activity log is the human-readable history -- a timeline of what agents did, when, and to what, the thing you scroll when a customer asks why they got a particular email. The runs API is the programmatic view: query runs, their states, and their details so you can pull agent activity into your own dashboards, alerting, or reporting. Between them you can answer both the ad-hoc question ('what did this agent do to this record?') and the systematic one ('show me every run that failed this week'). For a business that has to answer to auditors or customers, that trail is the evidence that a real human-oversight process exists and works.

Logs are pull; report-back is push. When an agent finishes a run, report-back has it summarize what it did in plain language and post that summary to the agent's chat thread, along with a notification -- so routine monitoring is reading a short briefing, not auditing raw logs. 'Processed 40 tickets, drafted 40 replies, flagged 3 for your review, one about a refund.' That's the daily read for a healthy agent.

Report-back is also how a scheduled agent stays visible. An agent that runs every morning at 7 and drops its summary into its chat thread with a notification is one you'll actually notice when something changes. If you want that recap to land in Slack or an inbox too, that's a separate step: the agent can post it there by calling the Slack or email integration as an action, the same way it uses any other tool.

Most monitoring tells you something happened. Usage caps do something about it. Every agent action is metered, and per-agent caps -- credits per day, credits per month, and tool-calls per day -- set a ceiling the agent cannot exceed. Hit the cap and the agent stops rather than continuing to spend. It's monitoring that enforces instead of just observing: the automated backstop for the failure mode where an agent loops or a bad prompt sends it into a spiral of expensive actions. The account-wide ceiling behind the per-agent caps is your team credit balance -- when it's gone, agents halt.

Pair caps with the max-iterations limit on individual runs -- set from 1 to 100 -- and you've bounded the damage at two levels: how far any single run can go, and how much the agent can do in total. Together they mean 'runaway agent' is a capped, known cost, not an open-ended one.

One agent is easy to watch. The reason you build observability properly is that you rarely stop at one. Deelo supports multi-agent setups -- a coordinator delegating to specialists that run in parallel and hand off to each other -- and the run model scales to that: each agent's runs are tracked individually, so you can see which specialist failed inside a larger delegated job rather than staring at one opaque result.

Monitoring is the last of the six deployment steps, but it's the one that feeds all the others. It tells you when to adjust autonomy, whether your approval workflow is keeping up, and what the agent is really costing. Watch it first, and every other decision has evidence behind it.