Background agents: the ones that don't wait to be asked

What makes an agent a background agent?

Most agents are reactive. Something happens in production, an engineer notices, and goes to the agent with a question. That's still the human reacting to the moment. A background agent flips it: it watches the sources itself, kicks off work when an event fires, and only pulls a person in when it absolutely has to. The agent becomes the one who reacts.

Where do teams point background agents first?

A few categories come up again and again:

• Deployment monitoring and the CI/CD system come first because deploys are the events most worth putting protections around.
• Feature flag changes and infrastructure changes get the same treatment, since they change production behavior just like a deploy.
• Operational handoff tasks, the routine work that normally lands on whoever picks it up next, can run without a human starting them.
• Passive service health monitoring rounds it out: an agent keeping tabs on whether things are healthy buys a lot of confidence and catches problems a little early.

What's the best way to work with a background agent?

A background agent is your coworker. There doesn't need to be a full configuration process. Instead, you literally just talk to it. One message is enough: "Give me a daily report at 9am. Check this dashboard, give me p99 latency and error rate, then flag anything that looks off against yesterday. DM it to me every morning."

The agent sets it up, shows you what the first report will look like, and when that isn't quite right, you just say so: "Hey, can you add a bit more information?"

It works the same way in the other direction. A background agent with access to a Slack channel is available to answer engineers' questions itself throughout the day because it already has the context: the most recent deployments, pulled together from the release history and the live state of the cluster, without anyone digging.

When agents aren't confident, they don't guess in public. It DMs the subject-matter expert first: "I think this is what I should answer. Is this the right answer?" Even when the expert still has to confirm, the figuring-out is already done, and everyone ends up operating at a higher level.

Why are background agents hard to build?

There are two different pieces to it.

• One is executing dynamic work. Foundation models are getting genuinely good at that part.
• The other is knowing what to do and why, which is where the hard part shows up in the operational world.

It comes down to a lot of small decisions. This metric is slightly high. Is that okay for this time of day, given the load we're running? The answer is all the context inside your environment.

Couldn't a team build background agents themselves?

Nothing says you couldn't build a lot of this yourself. The catch is that the good micro-decisions come from what sits underneath: knowledge systems that know what normal looks like in your environment, and integrations deep into all the tools where the answers live.

That's the part Resolve AI has already built, and it's why its agents can make better micro-decisions than a model on its own. Try building it, and you're going to get knee-deep in those same knowledge systems and deeper integrations.