Operations | Monitoring | ITSM | DevOps | Cloud

Harness AI is starting 2026 by doubling down on what it does best: applying intelligent automation to the hardest “after code” problems, incidents, security, and test setup, with three new AI-powered capabilities. These updates continue the same theme as December: move faster, keep control, and let AI handle more of the tedious, error-prone work in your delivery and security pipelines. ‍

Test coverage is one of those metrics everyone agrees matters until it’s time to actually write the tests. Between shipping features, fixing bugs, and handling production issues, writing comprehensive tests for edge cases and error paths often falls to the bottom of the backlog. The result is coverage gaps that accumulate technical debt and leave your codebase vulnerable to regressions. As AI-powered development tools reshape how we write code, the volume and velocity of changes is accelerating.

Bugs hide in plain sight. A date validator that rejects February 29th on leap years. An edge case that slips through code review. A flaky test that passes locally but fails in CI. These issues erode trust in your codebase and waste hours of debugging time. In the era of AI-assisted development, code is being written faster than ever. But speed creates risk.

This third article in our Agentic AI Essentials series lays out a process for capturing feedback and setting out a clear framework for effective use of an agentic system. It’s more than just collecting data; it's building a system you can trust. Let’s explore what success looks like in your environment.

Discover how Qovery leverages its own platform to accelerate AI development. Learn how an AI specialist deployed a complex stack; including LLMs, QDrant, and KEDA - in just one day without needing deep DevOps or Kubernetes expertise. See how the "dogfooding" approach fuels innovation for our DevOps Copilot.

Generative AI enables teams to write and ship code faster than ever. But current methods for testing and quality assurance have not evolved to match the new pace and scale of deployments. Manual and deterministic testing paths quickly become obsolete when new features are released, and they fundamentally can’t test AI outputs, leaving a massive untested surface area. To keep up, teams need new testing methods that can define what goals users have, and ensure that their outcomes match.

Prompt engineering improves single responses, but agent performance is determined by how execution context is captured, replayed, and constrained over time. For the past few years, enterprises have obsessed over prompts, with entire roles emerging around their design and an ecosystem of tooling and templates following close behind. This focus delivered early gains because it allowed teams to rapidly improve outputs without modifying the surrounding system. Over time, those gains flattened.