AI Agent Productivity Platforms in 2026, A Buyer’s Guide and Comparison

If you want to know how fast your engineers ship code, use a tool like DX or LinearB. If you want to know what AI tools, AI agents and human effort are delivering across sales, support, marketing and engineering, you need a more robust AI productivity platform. And we built Larridin to answer the enterprise AI ROI question.

The gap between engineering only versus enterprise wide platforms defines the category in late 2026. A 2025 MIT study found that 95% of enterprise generative AI pilots failed to show a measurable return. Many AI productivity platforms, such as DX, Jellyfish, Faros AI, LinearB and Swarmia began as developer productivity tools that added AI tracking later.

At Larridin, we took a different approach. We built Larridin to measure human and agent output together, across every department that spends on AI.

This guide compares Larridin with DX, Jellyfish, Faros AI, LinearB and Swarmia. It considers platform scope, AI agent visibility, pricing and real review data from G2 and Gartner Peer Insights. We want you to find the platform that best fits how your company uses AI, even when that answer isn’t us.

What is an AI agent productivity platform?

An AI agent productivity platform goes beyond tracking who has access to AI. These platforms measure how much value humans and AI agents actually return on the tools, tokens and licenses a company pays for. In short, what’s the bang for the AI buck?

The category grew out of engineering intelligence software initially built to measure developers' shipping speed. As companies rolled out AI coding assistants, chat agents and autonomous workflows, the platforms started tracking a second workforce, the agents working alongside people.

How do we measure if our ai agents are actually improving productivity?

Today most AI agent productivity platforms sit somewhere on a spectrum. On one end are engineering-only tools that treat AI adoption as one more developer metric next to cycle time and pull request size. On the other end are platforms thoughtfully built to track AI and agent output across multiple departments. Since AI spend is rarely confined to engineering once a company gets past its first pilot, we built Larridin to measure that end of the spectrum.

Reviewers Gartner and G2 haven’t settled on a single category name for this space yet. You may find it listed as “developer intelligence,” “engineering intelligence,” “software delivery intelligence” or “AI value measurement” depending on where you look. Whether you’re searching for AI agent productivity software, an AI adoption measurement platform, AI spend tracking or enterprise AI ROI measurement, start by assessing the products offered by these six companies.

What to look for in an AI agent productivity platform

Here are seven questions to help determine if an AI agent analytics tool can actually provide an AI ROI measurement versus ones that just add another dashboard.

1. Does the tool track AI agents as their own category, separate from human headcount, or does it fold agent activity into general usage numbers?

2. Does it cover departments outside Engineering, like Sales, Support and Marketing, or does it stop at the software development lifecycle?

3. Does it show spend by team, tool and individual agent or just total seat licenses?

4. How much setup work does it need? Some platforms connect to Jira and GitHub in an afternoon. Others need weeks of configuration to map an organization chart correctly.

5. Does it produce reporting a CFO or board member would actually read, or is the dashboard built for engineering managers?

6. What do real deployments say in G2 and Gartner Peer Insights reviews? How do the real word reviews compare with vendor provided case studies?

7. Is pricing public, or does every quote require a sales call?

There is no one size fits all answer to these questions. A 40-person engineering team evaluating AI coding assistants has different requirements than a 3,000-person enterprise trying to justify its AI spend to its board. These six platforms, ours included, answer them very differently.

Comparing the 6 AI agent productivity platforms in 2026

Here’s how Larridin and five other productivity platforms compare.

Larridin measures AI and agent ROI enterprise-wide

Larridin measures human and AI agent productivity together across an entire company. Engineering is included but it’s not the only department we measure. Our platform is built around four key metrics:

• Scout, our core platform where usage and adoption data lands.
• Spend and token intelligence, which reconciles token, seat and API costs across vendors.
• Work intelligence, which tracks hours returned and workflow changes by department.
• Engineering intelligence, which connects code output and delivery performance to AI spend.

Russ Fradin and Jim Larrison founded Larridin after building digital measurement infrastructure together at Comscore. Our CTO, Ameya Kanitkar, previously led engineering at LinkedIn and Coinbase. We raised a $17 million seed round led by Andreessen Horowitz, Bloomberg Beta and Gradient. Our customers include Klaviyo, SurveyMonkey, EcoVadis and Gainsight among others.

Best for: teams that need to show a CFO or board what AI spend is returning, across every department, in hours saved, workflows automated or ROI impact.

Where we fall short: we don't have a public G2 review base yet. And like most enterprise platforms, our pricing model is not a monthly subscription rate; it requires a sales conversation.

DX (GetDX) tracks developer experience and AI code output

DX built its reputation on developer experience surveys and support for the DORA and SPACE frameworks. It later extended that into AI code analytics, tracking how much code AI assistants generate and how that output holds up in review. It integrates deeply with the Atlassian ecosystem, and the review data backs it up: 4.6 out of 5 stars across 342 reviews on G2, the most mature review base in this category.

Best for: engineering organizations, especially ones already living in Jira and Confluence, that want research-backed developer experience data alongside AI code metrics.

Where it falls short: DX only measures engineering. It does not address AI usage in sales, support or finance. DX cannot provide a complete picture of AI agents impact companywide like Larridin can. See our full DX comparison for a deeper feature breakdown.

Jellyfish turns engineering activity into board-ready numbers

Jellyfish’s signature move is translating engineering activity into an FTE-equivalent breakdown by investment category. It’s the kind of number a CFO can put straight into a board slide. G2 reviewers rate it 4.5 out of 5 across 429 reviews, the largest review base of any platform in this guide. Several reviewers note that rollup figures and per-tool figures can diverge by roughly 10 percentage points. That matters when the aggregate number is what lands in front of the board.

Best for: engineering leaders who need to defend headcount and investment allocation decisions.

Where it falls short: Jellyfish’s investment-category model is built around engineering organization charts. It doesn’t address how sales or support teams use AI agents, which Larridin covers. Our Jellyfish comparison covers it in more detail.

Faros AI lets you build a fully custom data model

Faros AI lets teams define their own data model and metrics instead of working inside a fixed dashboard. Reviewers consistently credit that flexibility, and just as consistently give it a steep learning curve. It holds the highest G2 star rating in this guide, 4.8 out of 5, though from a much smaller base of only 12 reviews. In 2023, the company raised a $20 million Series A to expand its engineering intelligence platform.

Best for: engineering teams with the internal expertise to configure a data platform themselves and a reporting structure that off-the-shelf tools don’t fit.

Where it falls short: the custom configurability means a longer setup, and coverage still stops at engineering, whereas Larridin provides enterprise-wide data. Read our Faros AI comparison for specifics.

LinearB automates workflows and coaches AI adoption

LinearB pairs standard DORA and cycle-time metrics with workflow automation and delivery forecasting. An MCP server feeds AI coding-tool usage into the same dashboards. It’s one of the few platforms with fully public pricing. The Essentials plan runs $29 per user per month for teams of 50 or more developers, and the Enterprise plan runs $59 per user per month, both bundling a set number of monthly AI credits. G2 reviewers rate it 4.6 out of 5 across 80 reviews.

Best for: engineering teams that want transparent, self-serve pricing and automated workflow nudges instead of a long procurement process.

Where it falls short: like the other engineering-focused tools here, LinearB’s AI tracking stops at code output and doesn’t follow AI spend into all the departments Larridin tracks.

Swarmia keeps metrics light for growing engineering teams

Swarmia keeps its core metrics free for companies with up to nine developers. Paid plans scale from there through modular add-ons, including a dedicated AI adoption and cost module priced separately from its core productivity metrics. Reviewers on Gartner Peer Insights rate it 4.6 out of 5 based on 18 reviews.

Best for: smaller or fast-growing engineering teams that want to start free and add AI cost tracking as needed.

Where it falls short: Swarmia’s AI tracking is an add-on module inside an engineering tool, not a ground-up measurement layer like Larridin’s model.

How pricing works across AI agent productivity platforms

Four of the six platforms in this guide, including Larridin, do not publish pricing. While this is the standard approach for enterprise software sold on annual contracts, it means the price you pay depends on how well you negotiate and perhaps on how many deals your sales rep needs to close that quarter.

LinearB and Swarmia are the exceptions, and their public numbers are useful data points. LinearB starts at $29 per user per month for Essentials and $59 per user per month for its Enterprise platform. Both are billed annually, with minimum team sizes of 50 and 100 developers. Swarmia is free for teams under nine developers. After that, Swarmia charges $45 per developer per month for its Standard plan. AI adoption and cost tracking is available on its own too, as a $5-per-developer add-on.

The real question is what you’re buying. Does AI cost tracking as an add-on make sense, or is a more comprehensive AI measurement tool more valuable? Swarmia prices AI cost tracking as a $5 add-on to a developer-productivity product. The Larridin product includes both AI and agent measurement across the enterprise. Both are workable models. A team that only needs to bolt AI visibility onto an existing eng-metrics tool gets a cheaper, faster path with Swarmia. A company trying to account for AI spend across the whole business needs a platform built around that question from day one. That’s the gap Larridin fills.

Engineering-only or enterprise-wide is the real decision

Five of the six platforms in this guide, DX, Jellyfish, Faros AI, LinearB and Swarmia, measure AI adoption as a subset of engineering productivity. That’s a design choice, not a flaw. These models were built to answer questions engineering leaders ask, and presumably they answer those questions well.

The problem arises once AI spend moves past engineering. A sales team running an AI SDR tool, a support team running an AI ticket-triage agent and a marketing team running an AI content pipeline all generate token spend and productivity claims. None of these five platforms were built to measure any of these applications. When a CFO asks for one number on total AI ROI, someone is stitching together several tools’ exports in a spreadsheet. That’s the exact problem we started Larridin to solve.

Larridin was designed to be more forward thinking. We know enterprise AI spend rarely stays inside engineering past the first year of rollout. A platform that can’t see agent activity in sales or support can’t give the CFO an accurate ROI. Whether that matters depends on how far AI has spread past your engineering team. If it hasn’t spread yet, an engineering-only tool is the most cost effective answer today. But next year may be different.

What G2 and Gartner reviews actually say

We read past the star ratings on G2 and Gartner Peer Insights for the engineering-focused platforms in this category. We looked at every individual review and G2’s own AI-generated pros and cons tags. This deep dive revealed three interesting patterns.

Reviewers top praise for many platforms was the convenience of having a ready made dashboard and not having to build that dashboard by hand. Reviewers at DX, Jellyfish, Faros AI and LinearB all describe this as the reason they bought the tool, regardless of which platform they chose.

Every platform’s top complaint is also the same: reviewers don’t fully trust how the AI-driven scores get calculated. One enterprise DX reviewer called the platform’s AI pull-request scoring logic vague and warned that cross-team benchmarking could cause more harm than good without context. Jellyfish reviewers identified the same problem, particularly when that number goes in front of a board.

Review volume tracks how long and how aggressively a vendor has run review-solicitation programs, not how satisfied customers actually are. Star ratings across these platforms sit in a tight 4.5-to-4.8 band, but review counts range from 11 to 429, a 39x spread. A high star rating on a small review base isn’t the whole picture. It’s one reason we haven’t rushed to build up our own review count before we had enough real deployments to back it up.

Which AI agent productivity platform should you choose

• Pick DX if your organization already runs on Atlassian tools and wants developer experience data backed by published research frameworks. You don’t need visibility outside engineering.
• Pick Jellyfish if your main use case is translating engineering headcount into investment categories a board already understands. You can live with occasional rollup discrepancies.
• Pick Faros AI if you have the internal capacity to configure a fully custom data model and your reporting needs don’t fit any off-the-shelf dashboard.
• Pick LinearB if you want transparent, self-serve pricing and workflow automation without a long procurement process.
• Pick Swarmia if you’re a smaller, growing engineering team that wants to start free and add AI cost tracking when you need it.
• Pick Larridin if AI spend and AI agents have already spread past engineering into sales, support, marketing or finance departments. You need one measurement layer, and one number, that covers all of it.

Here’s where to start. Ask how many departments in your company are actually running AI agents today. If the answer is “just engineering,” one of the five tools above will serve you well and cost less. If the answer is “more than engineering and we can’t figure out the ROI,” that’s exactly the question Larridin was built to answer.

Where this category is headed

The AI agent productivity category is young enough that none of these six platforms will look the same a year from now. DX, Jellyfish, Faros AI, LinearB and Swarmia are racing to extend engineering metrics into agent tracking. Larridin is racing to extend agent tracking into every department that spends on AI. What we all have in common is the desire to give boards and CFOs the proof, not just projections, that AI spend is paying off.

For more on how the six platforms in this guide compare, feature by feature, see our complete competitive comparison.

Ready to see what enterprise-wide AI and agent measurement looks like for your own organization? Request a Larridin demo and we’ll walk through your specific stack.