Jellyfish Advances Developer and Agent Productivity Insights for the AI-Native SDLC

Engineering intelligence platform's new and updated features include lifecycle explorer, AI skill adoption measurements, and AI spend-to-work attribution

BOSTON, Oct. 7, 2026 /PRNewswire/ -- Jellyfish, the leading Software Engineering Intelligence and AI Impact Platform, today announced a comprehensive suite of features to help organizations measure the impact of AI engineering tools. The features, launched during Jellyfish's inaugural AI Impact Week, empower engineering teams to go beyond measuring adoption to understand how their organizations are transforming with AI and whether those investments are delivering value for the business.

"The software development process continues to be transformed by AI, and the industry's standard metrics, tools, and processes have struggled to keep pace," said Andrew Lau, CEO of Jellyfish. "Our platform makes it possible for engineering leaders to understand what's actually happening in the AI-native SDLC, from adoption to productivity to cost to ROI. Most importantly, it is designed to evolve with the industry and keep pace with every new tool, model release, and workflow."

Where Do I Stand: New Features to Measure Granular Usage

Jellyfish now ingest signals from across the engineering stack so leaders can see human work, AI-assisted human work, and fully autonomous agent activity side by side.

• Lifecycle Explorer: A top-down view of where time actually goes across the AI-DLC. Helping engineering leaders avoid the most expensive mistake in AI investment right now — buying more code generation when review is the bottleneck.
• AI Cohorts: Designed to segment and track how developers interact with generative AI coding tools including GitHub Copilot, Cursor, and Claude Code to help engineering leaders understand adoption and impact.
• Metrics Explorer: Complete analysis across human contributors and fully autonomous agents, providing a clear, top-level picture of how AI is impacting overall velocity. Users can also create Custom Metrics by describing what they want in natural language and letting the platform build it.
• Jellyfish Assistant and Agents: AI-powered chat interface that proactively surfaces insights, delivers trusted answers, and builds what you need to see based on each organization's engineering context.
• Research Insights: In-context research insights within the Jellyfish platform allow users to compare AI use against more than 1,300 other companies on the Jellyfish platform.

"Jellyfish's new AI features give us the ability to synthesize data that exists in Jira, Cursor, and GitLab repositories, combine all of that together and simply ask a question," said Bill Pawlikowski, VP of Engineering at Daxko. "The fact that we can ask a question and get an answer from that ecosystem has been mindblowing."

Am I Transforming: Understanding How Teams Work with Agents

Measuring throughput in an agentic world is now table stakes for engineering organizations. Jellyfish now helps leaders understand how their teams work with agents and optimize that activity.

• Skill Adoption: Real-time tracking of which AI skills and practices are spreading across an engineering organization, which teams have them, and which teams are falling behind.
• Behavioral Metrics: Designed to assess how well human engineers are working together with AI agents. Allows engineering leaders to understand which teams are compounding growth with AI and which are spinning their wheels.

"We've rebuilt Jellyfish from the ground up," said Adam Ferrari, SVP Engineering at Jellyfish. "Rearchitected on an AI-native data foundation, our platform goes beyond engineering analytics to help R&D leaders deeply understand, measure, and transform their AI practice."

What Is It Worth: Tracking AI Cost and Where Investment is Flowing

Many engineering organizations still struggle to track spending on AI tools due to differences in pricing models and spending reports. Jellyfish allows engineering organizations to view all AI spending in one place, including reconciliation between API-reported and telemetry-reported cost.

• Token Usage and Spend: Track token use by tool and by model, allowing engineering leaders to see which teams are getting real leverage and scale accordingly.
• Spend-to-work Attribution: Go beyond how much organizations are spending to track where AI spending is going — which initiatives, which deliverables, and which parts of the roadmap.
• AI Cost Benchmarks: Allows organizations to compare spend, outcomes, and spend efficiency with hundreds of industry peers.
• Total R&D Cost: See AI spend in the context of total R&D cost (People + AI), so the business can make informed decisions with the full scope of investment in mind.
• AI Capacity: A measure of change in capacity using AI that normalizes output to headcount, feeding more accurate headcount planning in this new AI world.

"More than two years into the AI revolution, R&D teams are still struggling to prove whether their AI investments are paying off," said Krishna Kannan, Head of Product at Jellyfish. "Engineering leaders don't need more reports, they need actionable insights. We built Jellyfish's suite of AI features to provide definitive answers on where and how teams are delivering value today."

Jellyfish's AI Impact Week, a three-day virtual event, includes live demos of new features Wednesday, Oct. 7, and concludes on Thursday, Oct. 8, with fireside discussions featuring leaders from AWS, Linear, IBM, Slalom, and CFGI.

About Jellyfish
Jellyfish is the leading Software Engineering Intelligence and AI Impact Platform, helping companies like DraftKings, Box, and Blue Yonder leverage AI to transform how they build software. By combining the industry's deepest engineering dataset with context-rich intelligence, Jellyfish helps R&D organizations understand what's driving impact, adopt proven industry best practices, and make smarter decisions across AI adoption, planning, delivery, and engineering performance.

Mark Dunphy
[email protected]

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SOURCE Jellyfish