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AI Agents Are Shipping Code. Can Your Metrics Keep Up?
Future-Proof Your Workflow with Weave Engineering Analytics
AI agents are opening PRs on your repo. Learn how Weave engineering analytics measures what actually matters in a world where humans and agents ship code together.
Your Next Teammate Won't Have a Slack Handle
A growing chunk of the pull requests landing in your repo this quarter won't be written by a person. They'll be created by an agent—an autonomous AI that can pick up a task from your backlog, write the code, open a PR, and move on to the next one.
In just a couple of years, we shot from simple autocomplete (like Copilot), to having full conversations about code (with tools like ChatGPT), and now to fully autonomous agents. The agentic era of engineering is here.
This new reality brings up a huge, urgent question for every engineering leader: If an agent can open 50 PRs in a single day, how do you know which ones actually matter? How do you measure your team's real output when "the team" is now a mix of humans and machines?
Every metric we've leaned on for years—commit counts, ticket velocity, cycle time—was built for a world where only humans wrote code. As our Guide to AI-Driven Engineering Analytics explains, these metrics were already showing cracks before agents arrived. Now, that human-only world is ending, and our old dashboards are about to become completely obsolete.
The Measurement Crisis No One's Talking About
This isn't a hypothetical problem for tomorrow; it's a full-blown measurement crisis happening today. The rise of AI in software development has come in three waves, and each one has made it harder to see what's really going on.
Wave 1 — AI Assistants (like Copilot): Lines of code became a totally meaningless metric almost overnight. A developer could spit out hundreds of lines of boilerplate code in seconds. Teams tracking "lines of code" saw their dashboards light up with what looked like massive productivity gains, but it was just noise. The metric was broken.
Wave 2 — AI Chat & Pair Programming (like Cursor): Developers started working in fundamentally different ways. Some went all-in on AI for brainstorming and refactoring, while others didn't. Comparing individual output suddenly became an apples-to-oranges mess.
Wave 3 — Autonomous Agents (That's now!): This is the biggest shake-up of all. Agents don't just help you code—they do the coding. They open their own PRs, refactor modules, and write tests. The central question for leaders is no longer "How productive is my team?" but "How productive is my entire system of humans and agents working together?"
Without analytics built for this new world, you're flying blind. The risk here is enormous. You might invest heavily in an AI agent that inflates PR counts but actually slows down delivery by burying your senior engineers in low-quality code reviews. The tradeoff of adopting agents without clear visibility is that you could accidentally disempower your human experts, losing sight of the creative, human-led problem-solving that actually moves the business forward [2].
This is a solvable problem, but only if your analytics are designed for this reality.
What "Future-Proof" Actually Means in 2026
When we say "future-proof," we're not talking about picking the perfect AI agent. Those tools will change constantly. A truly future-proof workflow has a measurement layer that gives you the ground truth, no matter how the work gets done.
Here’s a simple way to think about it:
A future-proof workflow can answer: Who did what (human or agent), how complex was the work, and did it actually make a difference?
A brittle workflow just counts outputs (like PRs) and assumes more is always better.
The risk of a brittle workflow is that you start optimizing for the wrong things. You might reward a team for shipping a high volume of agent-generated code, while accidentally penalizing the deep, complex human work that drives real innovation. The teams that win in this new era won't be the ones with the most agents. They'll be the ones who understand how to make their humans and agents collaborate effectively. In this world, clear visibility isn't a luxury—it's your biggest competitive advantage.
How Weave Gives You Clarity in the Agentic Era
This is exactly the problem Weave engineering analytics was created to solve. Weave isn't some old-school analytics tool that bolted on a few AI features. Our platform was designed from day one for a world where AI is a core member of the engineering team.
It Understands Complexity, Not Just Activity
Here’s the key difference: Weave uses its own specialized AI and LLMs to analyze what’s inside every single pull request. It doesn't just count lines of code; it understands the complexity, novelty, and business impact of the change. It knows the difference between an agent opening 30 PRs for minor typo fixes and a human opening one PR that refactors a critical authentication service.
This is why our analysis has a stunning 0.94 correlation to actual engineering effort [1]. It measures what really matters, showing you how Weave's AI powers the best engineering teams by focusing on impact.
It Answers the Questions Agents Make Urgent
Your team just adopted an agent for test generation. Two sprints later, cycle time actually went up. Why? Weave's Intelligent Team Insights shows your senior engineers are now spending 60% of their time reviewing agent output instead of building. Instead of drowning in output metrics, Weave gives you the story behind the numbers—exposing hidden bottlenecks and surfacing where agent-driven volume is impacting your team's focus.
Features like our Expert Engineer Index and Intelligent Team Insights cut through the noise to give you these answers automatically. Weave delivers the ground truth on your entire engineering system—both human and AI.
It Protects Your Humans (Not Surveils Them)
Let's address the elephant in the room: surveillance. In a world full of agents, the real danger isn't tracking your people too much. It's losing sight of their contributions entirely.
Picture this: At sprint review, your senior engineer's critical architectural decision is buried under the avalanche of 40 agent-generated PRs. What actually matters risks going unnoticed. In day-to-day practice, thoughtful, high-judgment human work becomes invisible, undervalued, or lost in the noise. The risk is that your best people get demoralized, burn out on reviewing low-quality AI output, and eventually leave. Weave engineering analytics flips that script. It acts as a spotlight for human expertise, ensuring that the critical architectural decisions, complex debugging, and creative problem-solving your people do are always visible and valued.
The goal isn't to rank people against machines. It's to design a system where both can thrive, which is central to how leading teams are rethinking engineering analytics.
Which Weave?
If you Googled "Weave" and landed on a page about MLOps, you're not going crazy! That's a different, open-source tool from Weights & Biases for monitoring ML models. Read more here. This article is about Weave (workweave.dev), the AI-powered engineering analytics platform.
The Teams That Measure the Future Will Own It
The shift to agentic engineering is happening right now. The metrics you rely on today were built for a human-only world—and will not survive this transition. Weave engineering analytics is the measurement layer built for what's next, giving you true visibility into the impact of all work—whether it's from a human or an agent.
The question isn't whether agents will change your workflow. It's whether you'll be able to see what's happening when they do. If you're exploring what's out there, check out this review of the top engineering analytics tools for 2026.
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Struggling with outdated metrics? Learn how Weave engineering analytics uses AI to measure true work complexity and future-proof your team's workflow.
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