How to Unlock the Power of Code Review Agent Insights in Jellyfish

Earlier this year Jellyfish launched a host of new AI Impact functionality including Multi Tool-Comparison, AI Spend and Code Review Agent Insights. Together these features give engineering leaders clarity around the adoption, value and impact of various AI tools and how they’re used across the software development lifecycle (SLDC) or, more aptly, the AI-DLC.

Today, we’re taking a closer look at Jellyfish’s Code Review Agent Insights to help clarify the impact these tools are having on engineering teams and their work.

Code Review Agent Adoption

Engineering teams across industries are embracing code review agents like GitHub Copilot – Reviewer, CursorBot, Graphite Diamond, Greptile, CodeRabbit, Bito, Gemini – Reviewer and others to accelerate feedback cycles and improve quality. But Jellyfish is the only platform able to quantify how – and how often – these agents are used.

Code Review Agent Insights helps engineering teams understand:

• How frequently AI agents appear in code reviews
• Where they’re used (down to the repository level)
• How adoption is trending over time, and more.

Review Speed and Quality

But adoption is only half the story. The next step is understanding impact.

Jellyfish data helps you see whether AI-assisted reviews are speeding up – or slowing down – PR cycle times. You can explore PR cycle times with and without AI to measure process efficiency, then pair those insights with Jellyfish’s Metrics Explorer to track downstream quality metrics. The goal isn’t just speed, it’s better outcomes.

You can also quantify the impact of a given code review agent on developer workflows to understand:

• How review processes are impacted by the tool, and
• If usage of these agents leads to better quality outcomes

Developer Sentiment and Code Review Agents

Every engineering team knows that tool adoption is as much cultural as it is technical. That’s why we’ve added new favorability and comment reaction metrics to show how developers are responding to agents over time.

If a particular tool’s feedback is consistently met with negative reactions, it might be time to reevaluate its value. On the flip side, positive engagement can signal that an agent is meaningfully improving the review experience.

With Code Review Agent Insights, you can answer questions like:

• Is the tool positively impacting developer workflows, or creating more toil? and
• Do we need to adjust the settings of the tool to more seamlessly integrate it into workflows?

Expanding Visibility Across the SDLC

Code Review Agent Insights marks a new chapter in Jellyfish’s AI Impact journey. We started by illuminating the impact of coding assistance tools – and now, we’re extending that visibility to the review phase of the SDLC. This broader focus reflects our vision: to give engineering leaders a holistic view of AI’s impact across the entire SDLC, from code creation to review, testing, and beyond.

What’s Next

In the coming months, we’ll continue expanding our AI Impact capabilities, helping organizations quantify impact across their full AI tech stack, not just tool by tool, but comprehensively across their engineering ecosystem.

Our mission remains the same: to help you measure, understand, and optimize how AI is shaping your teams, your processes, and your outcomes.

Getting Started with Code Review Agent Insights

For Jellyfish AI Impact customers, connecting Jellyfish to a code review agent used by your team is easy. Simply navigate to AI Impact within the Jellyfish platform, select Code Review Agents from the left-hand drop down, click Settings and enable the code review agent connection.