Operations | Monitoring | ITSM | DevOps | Cloud

Enterprise AI adoption is accelerating, but are organizations actually solving business problems or just adding more tools? In this episode of Agents of IT, Fran Fernandez (Chief Product Officer at Resolve) and Zach Austin (Director of Product Marketing) explore one of the biggest challenges facing enterprise IT in 2026: AI tool sprawl. They discuss why many organizations struggle to demonstrate ROI from AI investments, how disconnected AI assistants create operational complexity, and why orchestration, automation, and context have become the real differentiators for enterprise AI success.

When a malicious package is first published, threat intelligence sources haven't flagged it yet – and every team pulling from a public registry is exposed during that entire window. The fix isn't faster scanning; it's a policy that holds new packages for a defined cooldown period before they're eligible to pull. By the time the window closes, the threat intelligence has caught up. Teams pulling direct from npm or PyPI have no equivalent enforcement layer – which is exactly how attacks like Shai-Hulud got in.

Today, we're launching Autonomous Worker Agents, AI agents that run as governed pipeline steps inside Harness. They inherit OPA policies, RBAC, audit trails, and scoped credentials from the first run. And because they live inside your Harness pipelines, they reason using the Harness Knowledge Graph: your services, deployments, incidents, and policies.

AI agents write broken code nearly 50% of the time. By adding a traffic-based deterministic evaluation, Speedscale boosted unsupervised bug-fixing quality from 51% to 77% in just 5 minutes. This helped slash token costs and eliminate rework without human intervention. Learn more: speedscale.com.

Tired of outage after outage? Use traffic replay to automatically generate regression tests for your CI pipeline in just a few clicks. Take back your time! Read more at speedscale.com.

Same repo. Same tasks. Same validation gates. The only difference? Where validation happened. Here's how Chunk sidecars returned feedback in 22 seconds versus about 69 seconds through a traditional CI pipeline, giving the agent enough time to catch the failure, fix it, and revalidate before pushing.

Autonomous IT starts now with LogicMonitor and Edwin AI, built to help IT teams monitor complex hybrid IT environments, discover root cause faster, reduce downtime, and prevent incidents before they impact revenue or brand reputation. See how LogicMonitor brings AI-powered IT operations, observability, and incident prevention together for modern infrastructure teams.