Operations | Monitoring | ITSM | DevOps | Cloud

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.

Engineering teams are burning through AI budgets with nothing to show for it — $100M across 10,000 engineers and no cost per run, no cost per outcome, just a number that keeps climbing. When it runs dry, your infrastructure upgrade gets cut. Harness ties every AI token to the outcome it created: cost per run, cost per resolved ticket, and anomaly detection before the invoice hits. One customer went from a $28,000 black box bill to $0.60 per ticket.

The era of token maxing is over. When Claude Fable 5 launched last week at $10/$50 per million tokens - double the price of Opus 4.8 - it was a clear reminder that the most powerful model isn't always the right model. Not every task needs the Ferrari. The fastest way to burn your Al budget is sending every request to the most expensive model by default. The real question for the next phase of Al cost management isn't "can this model do the job?" — it's "is it the right model for the job?".

Harness has been named a Leader in the 2026 Gartner Magic Quadrant for DevSecOps Platforms — for the third year in a row. Here's what stands out: almost every company in the Leaders quadrant is missing a piece. Some have Dev, some have Ops, but not the security. Harness is the one platform that brings Dev, Sec, AND Ops together — with AI built in. AI is changing how software gets built: more code, more automation, more agents — but also more complexity. Teams need delivery, security, testing, reliability, and cost in one place. That's what we're building every day.

Will AI replace engineers? Hugo Charré from Cursor argues the opposite. In this clip from the upcoming summit, Hugo Charré (Cursor) explains why the number of engineers is going to grow as we scale agentic systems — and why AI raises the bar for who's on the team.

Your entire AI stack ran on a model that disappeared in three days. The US government issued a directive suspending all access — a few hours' notice, no deprecation window, no roadmap. Launched Tuesday. Gone by Friday. And every enterprise that had built workflows on top of it just found out what the real risk was: not the model itself, but the absence of a governance layer underneath it.

Your best engineer spent 500,000 tokens last week. Nothing shipped. There's a name for it now: tokenmaxxing. Failed prompts, dead PRs, code that never reaches production — it looks like productivity, but it isn't. Most engineering leaders can't tell you what percentage of AI-generated code actually ships, or where the budget went. You should be able to say "that bug cost me $2,700 in tokens to fix.".

Bain says AI cost savings aren't arriving. But the value isn't missing, it's invisible. Most engineering teams can see token spend. They can see AI usage. What they can't see is whether any of it shipped, and whether it moved the needle on delivery. That's the measurement gap. And until it closes, AI ROI will keep looking worse than it should.

Artifact governance should not depend on manual checks. But for many teams, container images, software packages, and open-source dependencies are imported into registries from multiple internal and external sources. Without automated guardrails, vulnerable images, untrusted packages, end-of-life dependencies, or non-compliant artifacts can reach developers and delivery pipelines.

Managing MongoDB database changes shouldn't require manually creating and maintaining changelogs. In this video, you'll learn how Harness Database DevOps automatically generates MongoDB changelogs, helping teams capture existing database changes and bring them into version control for reliable CI/CD workflows. As a modern **database schema migration tool**, Harness Database DevOps helps teams automate database change management across relational and NoSQL databases, reducing manual effort and deployment risk.