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Engineers write tests for the logic that worries them and let the small details slide. One of our commerce engineering teams now has a scheduled agent that finds the thinly tested files, writes the missing tests, and opens a pull request for review. Nobody decides to skip a unit test. The core logic gets covered, the small details don’t make the cut, and most days that’s a fair trade.

The first minutes of an incident are spent asking questions a monitoring tool can answer but rarely in the form you asked them: which servers are in trouble right now, is it the same thing as last night, what did memory do on the web servers before it started? Each answer is a few clicks away, on a different page.

AI dashboards use artificial intelligence to surface relevant metrics, generate visualizations, and add context to complex data. Learn how AI dashboards work, how they compare with traditional dashboards, and what they can mean for IT operations.

Say you want to build an agent that resolves support tickets against your own data. Not a demo against a toy dataset, an agent that actually runs in production. The agent itself is maybe a day of work.

Aiven Managed Agents: Your agents, your data, fully managed and open-sourced.

Yesterday, my partner told me about his new automated pipeline from a conversation recording to publication of a whole podcast, with social media blurbs and graphics and everything. It did more than he'd predicted the agent could do. "We are limited by what we can imagine," he said. Cheesy, but it kinda gave me goosebumps because it's (almost) true. Most of the experienced developers I know are concerned.

You've just finished a deployment, and though your service is up, you start to notice some requests are getting slower. To address the lag, someone—or, more likely, some group of people on your team—needs to watch the dashboards, decide whether the change matters, investigate what happened, and turn the findings into a fix. At each step you're gathering context—and often explaining that context again to the next person or tool.

A no-code AI agent builder can get a defined agent into production quickly. The risk appears when added branches, exceptions, integrations, and controls exceed the visual canvas. Organizations should look for a platform that lets teams extend individual steps with reusable code without rebuilding the agent or abandoning its governance.

The problem: our AI spend was growing faster than our understanding of its ROI. Like most engineering organizations, Harness went from "a few people are trying out AI coding tools" to "AI tooling is a line item in the monthly finance review" in a matter of months. The cost itself wasn't really the problem.