Tired of Engineering Dashboards That Don't Tell the Whole Story?

As an engineering leader, you've got dashboards for everything: cycle times, PR counts, deployment frequency. But have you ever stared at a chart, seen a metric dip, and just thought, "...so what?" You can see what happened, but you have no idea why.

The problem is that traditional analytics are lagging indicators. They show you the effect but not the cause. This leaves you in meetings where the best anyone can offer is speculation about why velocity dropped 20%. Guesswork isn't just inefficient—it can lead you to chase the wrong problem entirely.

This is exactly where Weave's AI agent, Wooly, changes the game. Weave provides AI analytics for engineering teams, and Wooly is your team's dedicated deep research agent [3]. It doesn't just show you a chart; it investigates your data to tell you the why behind the numbers. Think of it as an embedded analyst who works 24/7, cross-referencing your GitHub, Linear, and Cursor data in seconds to deliver real intelligence, not just more data points.

Moving From Surface-Level Metrics to Deep Intelligence

Relying on old-school metrics often feels like flying blind. Dashboards might flag that velocity is down, but this just creates more questions than answers and can lead to gut-feel decisions dressed up as data-driven ones.

This is especially true in the age of AI. We’ve known for a while that metrics like lines of code have a weak correlation with actual effort (around ~0.3). Even story points only bump that up to ~0.35. As our guide to AI-driven engineering analytics explains, these numbers fail to capture the complexity or true value of what your team accomplishes.

The Wooly way is different. Wooly acts as an analyst, using parallel AI agents to dig into your connected systems and find the root cause. It's not just about what happened, but why it happened and what you can do about it [2]. It connects the dots—correlating a spike in review time with an overloaded reviewer or linking a velocity dip to a series of complex infrastructure PRs from a planned refactor.

What Wooly Investigates for Your Team

To get the complete story, Wooly consolidates data from all the places your team works. It’s not just looking at Git history; it’s building a holistic view by integrating with tools like GitHub, Linear, and Cursor. This allows it to pull together code activity, project management data, and AI tool usage into a single intelligence layer, turning raw engineering data into actionable intelligence.

Understand True AI Adoption and ROI

Today, nearly every engineering organization has invested in AI tools like Cursor, Copilot, or Claude. The problem? Most leaders have no real visibility into whether those tools are actually moving the needle. You need to know if you're getting real value or just encouraging developers to generate more code without purpose.

Wooly analyzes work patterns to show you how AI is really contributing. It helps you answer critical questions like:

• Is our investment in AI tools actually paying off?

• Which teams are leveraging AI most effectively?

• Are AI-assisted PRs higher quality, or just faster to merge?

This turns a vague hunch about AI's value into the concrete data you need to prove the ROI of your AI software engineering tools.

Diagnose Fluctuations in Team Velocity

Imagine your team's velocity suddenly drops. A traditional dashboard just shows a red number, triggering a stressful guessing game. Instead of guessing, Wooly investigates. It analyzes factors like:

• A sudden increase in PR complexity and scope as the team shifted from small features to a complex refactor.

• Changes in code review patterns, identifying a specific senior engineer who has become a bottleneck.

• A shift in team composition, like a key member being on PTO or a new hire who is still ramping up.

Without this context, it's easy to make the wrong call. For example, Mycroft's CTO Jan used Weave to validate a major infrastructure overhaul their team undertook in late 2024. The work temporarily dropped their visible output, which would have looked like a problem on a typical dashboard. With Weave, he could see that the dip paved the way for performance gains that exceeded top benchmarks in Q1 2025. It's a perfect example of why teams are moving beyond simplistic measures like story points and toward a more intelligent analysis of work.

Pinpoint Bottlenecks in Your Delivery Cycle

Most teams know their cycle time is "too long," but they can't pinpoint where the time is being lost. This leads to slow delivery and developer frustration.

Wooly helps you see exactly where work gets stuck. It doesn't just tell you "review time is up." It tells you which reviewer is the bottleneck, which repos are the worst offenders, and what changed to cause the slowdown. These are the kinds of specific, actionable insights surfaced directly in your team and individual engineering dashboards that you need to make real improvements.

How It Works: Your AI-Powered Research Agent

So, how does Wooly find these insights? The process is designed to be both powerful and simple.

1. Wooly connects to your engineering tools (GitHub, Linear, Cursor, etc.) to build a unified model of your team's work.

2. When it detects an anomaly or you ask a complex question, it dispatches up to six parallel AI agents.

3. Each agent investigates a different angle: one analyzes code complexity, another correlates it with project tickets, a third examines AI tool usage, and so on.

4. Wooly synthesizes the findings from all agents into a single, easy-to-understand story with root causes and suggested actions.

Wooly also uses intelligent routing. Simple lookups get fast answers, while complex investigative questions trigger the full deep research pipeline. This keeps the platform responsive without sacrificing depth when it matters. You can read more about this in our post on building the deep research agent.

Ready to Stop Guessing and Start Knowing?

With traditional engineering analytics, you see the data. With the Weave engineering analytics platform and Wooly, you get intelligence. It transforms your team from being reactive ("Velocity dropped, let's figure it out next week") to proactive ("Wooly flagged a review bottleneck—here's the cause and how to fix it before it impacts delivery").

What could your team accomplish if you knew the "why" behind every change?

Learn more about the Weave platform to see Wooly in action, and follow us on LinkedIn for more updates [1].

Meta Description

Go beyond basic dashboards. Wooly enhances Weave engineering analytics with AI to find the 'why' behind your data and improve team performance.

Citations

[1] https://www.linkedin.com/company/weave-dev

[2] https://www.linkedin.com/posts/adam-b-cohen_most-engineering-analytics-tell-you-what-activity-7391925273170132992-pJu9