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AI Productivity Metrics Dashboard for Engineering Managers (2026)
Measuring AI’s impact on your engineering team is harder than it sounds. Headlines claim AI writes 30% of code and doubles productivity, but those numbers rarely match what you see on the ground. Without a dedicated dashboard that blends leading indicators, anti-gaming safeguards, and ROI reporting, you cannot answer the question that matters most: is AI helping your team ship better software faster?
Get Lightrun AI Skills: Expert Workflows for AI Agents
Today we’re launching Lightrun AI Skills, structured, repeatable investigation workflows built for AI coding agents. With Lightrun MCP, agents like Claude Code, Codex, and Cursor can already instrument live production services and reason over live runtime evidence without a redeployment. But AI agents remain non-deterministic by design, using the same tool differently every session.
AI, Platforms, and the Future of Value Delivery: A Conversation with ServiceNow
How do enterprises turn AI from experimental potential into real-world software delivery value — without slowing down, breaking security, or sacrificing reliability? At {unscripted} 2025, Amit Zavery — President, Chief Product Officer, and COO of ServiceNow — joined Harness CEO and Founder Jyoti Bansal for a candid fireside chat on the future of AI in the enterprise, the role of platforms in unlocking developer productivity, and why"AI-native" only works when speed, security, and reliability move together.
Action trails: The missing link between AI and human trust
When people talk about trusting AI, they usually focus on the interface. It summarizes and uses confident language with a level of clarity that feels reliable. But that’s all window dressing. None of it builds trust. Trust doesn’t come from what the AI says. A verifiable record of what the AI did makes it trustworthy.
What broke when engineering went fully agent-based
Last year, we went fully agent-based at Rootly. Cursor, Claude Code, Codex, all of it. The productivity gains were real. However, Rigel, senior engineering manager at Rootly, started noticing a pattern emerging in his team.
The "Free" AI Tool That Will Ruin Your Code#speedscale #aiagents #aicoding #devops #softwareengineer
Relying on AI and interns to build custom traffic replay tools is a scalability nightmare that introduces security risks, brittle code, and massive maintenance costs...use Speedscale instead. Learn more: speedscale.com.
When your agents hallucinate at 2 am, it is not a model problem
The first time an AI assistant suggests "restart the service" during a live incident and nobody on the bridge can tell whether that suggestion came from a current runbook, a stale wiki page, or thin air, you stop caring about model benchmarks. You start caring about what the agent actually knew, where that knowledge came from, and whether you can trust the chain of reasoning behind it.
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.
Why agentic AI development needs reliability guardrails
AI has massively accelerated code deployment. In fact, since the introduction of agentic coding, GitHub has seen exponential growth in PRs, commits, and new repos. What they originally predicted would require 10X capacity, they’re now estimating it’s going to require 30X capacity, and the biggest driver is agentic development. Companies across industries are building agentic pipelines to ship features faster than ever before. That acceleration isn’t without risk.