Always on agents for daily engineering tasks in production

Most agents wait for a human to decide there is work. That is the constraint worth removing.

What is a background agent?

An agent that initiates its own work from a trigger rather than waiting to be asked. Every agent loop needs something to start it, and today that something is usually a person who has already noticed a problem and decided an agent should look at it. A background agent watches other sources instead, and starts working when one of them indicates there is something to do.

The consequence is a change in who reacts first. Engineers spend a large share of their time being pulled into events they did not choose, responding in the moment. When the agent is the thing reacting to the event, the person gets pulled in only at the point where they are actually needed.

What should you point a background agent at first?

Deployment monitoring is the highest-value starting place for most teams. That means putting protections around the moments when something changes: a release going out through CI/CD, a feature flag being flipped, an infrastructure change landing. Almost every environment has these, and almost every environment has incidents that trace back to one of them.

Operational handoffs are the second category. Passive service health monitoring is the third, and it earns its place less by catching disasters than by catching things early and giving a team a baseline of confidence about whether things are healthy.

How do you set one up?

By describing what you want in conversation rather than filling in a configuration. You tell the agent you want a daily report at nine, which dashboard to check, which questions to answer, and to send it as a direct message. If the reports start arriving and they are thinner than you wanted, you say so and it adjusts.

The difference from configuration is not only convenience. Tuning a workflow usually means going back into a settings interface and reasoning about fields, so people do it rarely. Tuning by conversation means the thing gets adjusted when it is slightly wrong instead of being abandoned when it is mostly wrong.

What have customers built that wasn't anticipated?

The one worth describing is Slack channel monitoring. Teams have pointed background agents at channels where engineers ask questions, and the agent answers the ones it has the context to answer.

The interesting part is what happens when it is uncertain. Rather than guessing or staying silent, the agent can direct message the subject matter expert with a proposed answer and ask whether it is right. The expert is no longer doing the work of assembling an answer. They are checking one, which is a much smaller task, and it moves the human up a level to judging rather than producing.

How is this different from a scheduled workflow or automation?

Scheduled automation executes a predetermined sequence. The value of a background agent is not that it runs on a schedule, since a cron job does that. It is that the work it performs is decided at the moment it runs.

Foundation models have become reasonably good at executing dynamic work. That part is no longer the bottleneck.

If executing the work isn't the hard part, what is?

Knowing what to do and why to do it. Operational work is made of small judgments that look trivial written down and are not: this metric is running slightly high, and whether that is a problem depends on the time of day, the current load, and what normally happens in this system under these conditions.

Every one of those judgments requires context that lives inside the specific environment. An agent that gets those micro-decisions wrong produces noise, and an agent that produces noise gets turned off. Resolve AI's position is that the knowledge systems and the depth of integration underneath the agent are what make those decisions good enough to trust, and trust is the gate on letting an agent take on longer and larger tasks.

Why is this hard to build in-house?

You can build a version of it. What you cannot easily do is stop building it. Getting a background agent into production means getting knee-deep in knowledge systems, tuning them against your environment, and pushing integrations deeper than a first pass requires.

The gap that catches teams is the one between a demo and a system. Building something that works impressively most of the time is not difficult. Making it run reliably and efficiently in production is a different project, and the majority of the value sits in the part that only shows up when the situation is difficult.