Better AI coding agents don’t automatically produce better engineering outcomes. DORA’s 2025 research found that AI acts as an amplifier: it magnifies existing organizational strengths and dysfunctions. Strong delivery systems can turn faster coding into better results, while weak systems can simply move problems downstream faster.
That’s why measuring how engineers work with agents requires more than watching general engineering activity. DORA’s software delivery metrics still matter, but they don’t directly explain whether a change came from a human, an agent, or both—or whether faster code generation created more pressure in review, testing, reliability, or quality. Higher activity can be a good sign or a warning, depending on what happens downstream.
If you’re asking “what’s the best tool to measure and improve how engineers work with agents,” here are five worth evaluating, along with what each one is built to see.
1. Larridin
Larridin’s AI-Native Developer Intelligence adds an AI-specific layer to traditional engineering metrics. It measures engineer-agent effectiveness, environment readiness, workflow bottlenecks, and token cost effectiveness, then connects those signals to delivery, quality, reliability, developer sentiment, DORA, and SPACE. Larridin’s preliminary 2026 benchmarking data shows why those layers matter: raw engineering activity increased about 12% from January through July, while complexity- and quality-adjusted Engineering Output increased about 22%.
Best for: Engineering leaders who want to know whether human-agent workflows are producing durable outcomes and whether their engineering environment is ready for agentic development.
Limitation: If your main need is a dedicated DORA dashboard or delivery-workflow automation, a specialized engineering platform may go deeper in those areas.
2. LinearB
LinearB combines DORA metrics and cycle-time tracking with gitStream, its workflow automation layer, and AI code review. That combination gives engineering teams a way to measure delivery performance and automate parts of the pull request and review workflow rather than relying on dashboards alone.
Best for: Engineering leaders who want workflow automation and AI code review alongside delivery metrics in one platform.
Limitation: LinearB is focused on software engineering workflows, not measuring how agents are used outside engineering.
3. Swarmia
Swarmia combines AI adoption and cost data with engineering metrics, investment-balance reporting, and developer experience surveys. It also emphasizes using engineering data to improve teams and systems rather than reducing developer performance to a single score.
Best for: Teams that want AI adoption and cost data alongside developer feedback and team-level improvement workflows.
Limitation: Swarmia is engineering-scoped by design, so it doesn’t extend into non-engineering AI usage.
4. Faros AI
Faros AI brings a telemetry-heavy view to AI-assisted engineering. Its AI Productivity Paradox research analyzed telemetry from more than 10,000 developers across 1,255 teams and found that higher individual activity didn’t translate into better company-level delivery metrics. The platform draws on signals across engineering systems to help leaders see where AI-assisted work is changing throughput, quality, and delivery.
Best for: Organizations that want systems-level visibility across a broad set of engineering telemetry.
Limitation: Faros AI is centered on engineering productivity and software delivery rather than AI workflows across the broader enterprise.
5. GetDX
GetDX’s AI measurement framework focuses on three dimensions of AI-assisted engineering: utilization, impact, and cost. It combines those AI-specific signals with DX’s broader engineering productivity frameworks, telemetry, developer surveys, and benchmarking.
Best for: Teams that want AI and agent measurement grounded in research-backed engineering productivity frameworks and a mix of quantitative and developer-reported signals.
Limitation: If your priority is codebase readiness or workflow automation, you may still need a complementary tool focused more directly on those areas.
What “Improve,” Not Just “Measure,” Actually Requires
The five tools differ most in what they help teams do after measurement. LinearB puts automation directly into review workflows. Larridin focuses on engineer-agent effectiveness and the conditions that determine whether agents can produce accepted, durable work. Swarmia pairs engineering metrics with developer feedback and improvement workflows. Faros AI emphasizes broad systems telemetry, while GetDX combines telemetry with research-backed survey and benchmarking frameworks.
That makes the buying question less about which platform has the most metrics and more about what you need to diagnose and improve. Are agents creating useful capacity or more review work? Is the codebase ready for autonomous work? Are quality and reliability holding as output increases? Do you need workflow automation, developer feedback, or broader telemetry to understand the problem?
The early-2025 METR randomized controlled trial is a useful reminder that perception and measured results can diverge: developers believed AI had sped them up by 20% after the study, while the measured tasks took 19% longer with AI. METR has since reported early evidence that newer tools may deliver modest speedups, reinforcing the larger point: agent performance changes with the tools, tasks, and environment. Teams need current measurement of their own work, not assumptions about how productive AI should make them.