Agents Could Kill Overhiring
Cloud killed the overprovisioned server rack. Agents might do the same thing for engineering teams.
Operations | Monitoring | ITSM | DevOps | Cloud
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The latest News and Information on Continuous Integration and Development, and related technologies.
Run your first microbuild in 5 minutes
AI coding agents produce code faster than most teams can validate it. Without a validation step between the agent and CI, every problem gets caught after the push, and feedback arrives long after the agent has lost context. Agents need consistent feedback while they’re working so that small failures get fixed locally and CI stays focused on moving code into production.
The Problem With AI Token Caps
Your engineers don't suddenly become less valuable because they hit a token cap.
Same team, but building more ft. Chris Kelly of Augment Code
Most teams obsessing over token costs are measuring the wrong thing. The real savings from AI aren't in lines of code written faster. They're in the coordination overhead that disappears when fewer humans need to align before anything gets built. Chris Kelly, Head of Product at Augment Code, joins Rob to cover why prototypes have replaced specs, how agents enable dynamic team capacity the way cloud replaced over-provisioned servers, and what "good code" even means when your primary reader is an LLM. In this episode.
Reduce CI Costs Without Slowing Down Development | Harness Blog
Continuous integration (CI) costs can escalate quickly as engineering teams scale. While most organizations focus on cloud bills, the true cost of CI includes slow build times, developer wait time, inefficient test execution, and overprovisioned infrastructure. CI cost optimization is the practice of reducing the total cost of CI pipelines by improving build efficiency, minimizing compute usage, and eliminating unnecessary work without slowing down development.
Agentic Pipelines now supports Claude Code
Last month, we introduced Agentic Pipelines, a new way to orchestrate AI agents to automatically, and routinely, handle the repetitive engineering chores so you can get back to solving the fun, cool problems. When we launched, Agentic Pipelines supported Atlassian’s developer AI agent, Rovo Dev. Today, we’re opening up Agentic Pipelines to even more teams: You can now run agentic steps in your pipeline with Claude as the provider.
Keep your Agents Under Control with agent-belt
You’re shipping a product with an AI-facing interface, or embedding AI-facing interfaces across your existing product line – skills your customers trigger, MCP servers their agent reaches for. Indie author or enterprise, your code runs in someone else’s agent runtime, against a model that updates every other day and a CLI that updates every other week. Cursor 2026.05.05-84a231c rolls out. Claude Code 2.1.132 lands the same week. OpenAI bumps gpt-5.5.
The Hidden Cost of Kubernetes: Why Your Cloud Bill Is 40% Higher Than It Should Be
The average enterprise running Kubernetes wastes between $2 million and $10 million annually — not from overspending, but from under-optimizing. This is the story of costs you can't see on your dashboard but that your CFO feels every quarter.
There's an npm-shaped hole in the AI tooling stack
I've had this same conversation with 60+ engineering teams in the last six months. A team adopts AI tooling. One developer figures out how to use it well, builds up a vault of skills, MCP configs, and slash commands that 10x their output. The rest of the team has whatever they can scavenge from a shared Notion doc.
How Engineering and Ops Teams Use OKRs to Connect Technical Work to Business Outcomes
Engineering and operations teams have a measurement problem that most other functions don't. The technical metrics are excellent. Deployment frequency is up. MTTR is down. Uptime is at 99.97%. The CI/CD pipeline is running cleanly and the on-call burden has been reduced by 30% since the team adopted a proper incident management process. By every internal measure, the team is performing well. And yet, in the quarterly business review, the conversation keeps returning to the same uncomfortable question: what did engineering actually deliver for the business this quarter?