These are the best platforms for measuring engineering performance:

1. Pensero

2. DX

3. LinearB

4. Jellyfish

5. GitHub Copilot Analytics

6. Faros AI

The software engineering role is not being replaced. It is being restructured, and the restructuring is happening faster than most organizations have updated their performance measurement frameworks to reflect.

Agentic AI systems, capable of sustained multi-step execution and autonomous reasoning, are now participating in every phase of the software development lifecycle: analyzing requirements, drafting technical specifications, writing first-pass implementations, generating test coverage, reviewing pull requests, and monitoring production systems. The engineer who spent their day primarily writing code is becoming the engineer who spends their day directing, reviewing, and validating what AI systems produce.

This shift is real and measurable. Pensero's 2026 Engineering Benchmark Report tracked continuous delivery data across thousands of engineers from November 2025 to April 2026. Average complexity-weighted delivery rose 34.2% at the industry median. The top 5% rose 51.4%. The performance gap between elite and average teams widened from 4.9x to 5.9x, and the acceleration maps directly to the period when AI-assisted and agentic development moved from experiment to default. The engineers and teams pulling furthest ahead are not those generating the most code. They are those directing AI most effectively while maintaining the judgment layer that agentic systems depend on.

The measurement challenge this creates for engineering leaders is significant. The frameworks used to evaluate engineers, commit frequency, PR volume, story points, were designed for a world where writing code was the primary constraint. In an agentic era, those metrics measure AI output at best and noise at worst. The human contribution that actually matters, the quality of orchestration, the depth of review, the architectural judgment, the knowledge transfer, is mostly invisible to activity-based measurement.

This article covers what role transformation looks like at different levels of seniority, what the new skill hierarchy means for talent evaluation, and how engineering leaders can measure performance in organizations where human engineers are directing AI systems rather than primarily authoring code.

5 Tools for measuring engineering performance in the agentic era

Measuring engineer contribution when AI is doing more of the implementation requires platforms that capture the full body of work, not just commits and PRs, but review depth, collaboration patterns, architectural influence, knowledge distribution, and the quality of what AI-generated code produces downstream.

Platforms built on activity counting are structurally unable to answer the performance questions that agentic development creates.

1. Pensero

Pensero is an empowerment tool for engineering performance that brings together real signals from GitHub, Jira, and the tools your team already uses to uncover how work moves, where it gets blocked, and how development practices and AI usage translate into real business impact.

Engineering output now comes from three distinct sources: human engineers, AI-augmented developers, and autonomous agents. Pensero distinguishes between all three and measures their real impact on delivery outcomes, not just activity volume. The platform separates AI autocomplete from full agent-driven workflows, tracking what share of delivery is driven by agentic development and what the quality and collaboration profile of that delivery looks like relative to human-authored work.

For agentic-era performance measurement, the collaboration signal becomes as important as the delivery signal. Engineers whose primary contribution is orchestrating agents, reviewing agent output, and maintaining architectural coherence show different behavioral profiles from engineers writing code directly, and Pensero captures both. Review participation, cross-team enablement, knowledge distribution across the codebase, and the defect rate of code areas that specific engineers have touched are all part of the measurement picture.

Knowledge gaps, the percentage of code areas with limited contributor diversity, are particularly critical in agentic environments where agents write code that human reviewers approve without deeply understanding. Pensero tracks this continuously, surfacing where the organizational knowledge base is becoming fragile as agentic adoption grows.

Pensero Calibrate enables the cohort comparison that role transformation measurement requires: put engineers who are effective orchestrators against those who are not, on delivery per headcount, defect rate, collaboration, and knowledge gaps. Put senior engineers who adopted agentic workflows early against those who have not. Put teams running agent-orchestrated development against those still on traditional SDLC. Each comparison shows the behavioral and outcome differences between groups, which is the data that turns observation about role transformation into decisions about development, hiring, and organizational design.

The platform integrates with GitHub, GitLab, Bitbucket, Jira, Linear, GitHub Issues, Slack, Microsoft Teams, Notion, Confluence, Google Drive, Google Calendar, Microsoft 365 Calendar, Cursor, Claude Code, GitHub Copilot, Gemini Code Assist, OpenAI Codex, and YouTrack. Zero configuration required. Customers include TravelPerk, ClosedLoop, Elfie.co, and Caravelo. Pricing as of July 2026: free tier up to 10 engineers and 1 repository; $50/month premium; custom enterprise pricing. Compliant with SOC 2 Type II, HIPAA, and GDPR.

2. DX

DX measures the experience side of role transformation, how engineers at different seniority levels perceive the shift in their responsibilities, what friction they encounter in agentic workflows, and whether the role change feels like elevation or displacement. 

For organizations managing the cultural and retention dimensions of agentic adoption, where senior engineers who built careers on deep implementation skills need to feel valued in an orchestration-focused role, DX provides the structured sentiment data that delivery metrics cannot capture. It answers how the role transformation is being experienced; Pensero answers what it is producing.

2. LinearB

LinearB tracks workflow patterns around the pull request pipeline. In agentic environments, it surfaces whether agent-generated PRs are moving through review faster or slower than human-authored ones, whether review wait times are increasing as agent submission rates rise, and whether specific engineers are becoming review bottlenecks as agent volume grows. 

For engineering managers focused on whether the workflow is functioning effectively as agentic adoption scales, LinearB provides the pipeline-level visibility. It does not connect those patterns to the complexity or quality of what was generated or reviewed.

3. Jellyfish

Jellyfish provides engineering investment visibility at the portfolio level, which in the agentic role transformation context addresses how engineering capacity is redistributing as agents take on implementation work. If agentic adoption is working, the investment allocation should shift: more engineering time going to planning, design, and review; less to implementation boilerplate. 

Jellyfish's allocation tracking can surface whether this redistribution is actually happening or whether agents are adding to engineering activity rather than substituting for the lower-value parts of it.

4. GitHub Copilot Analytics

GitHub's native Copilot analytics surfaces how engineers are interacting with Copilot's agentic capabilities, invocation frequency, task types, and acceptance patterns. 

For organizations using Copilot's agent mode as the primary agentic workflow, the native dashboard provides the usage-level view of adoption. It does not connect that usage to delivery quality, knowledge gap accumulation, or the downstream outcome patterns that determine whether agentic adoption is genuinely improving engineering performance.

5. Faros AI

Faros AI applies causal modeling across engineering data sources to attribute delivery changes to specific factors, including the transition to agentic workflows. 

For organizations that need to rigorously demonstrate that the performance changes observed are caused by agentic adoption rather than other concurrent organizational changes, Faros provides the analytical depth that correlation-based approaches cannot. Best suited to organizations with dedicated data engineering capacity.

How the role is changing by seniority

The impact of agentic AI is not uniform across engineering levels, and performance measurement needs to account for how different roles are transforming differently.

Junior engineers are experiencing the most significant shift in what their role demands. The traditional junior contribution, learning syntax, writing boilerplate, implementing clearly defined tickets, is being automated at exactly the entry point where junior engineers would previously have built foundational skills. The new entry-level skill set is different: understanding how to prompt agents effectively, debugging AI-generated outputs that fail in non-obvious ways, and developing context engineering skills, the ability to give agents the precise technical and business context they need to produce reliable output.

This creates a genuine measurement challenge. A junior engineer who is excellent at context engineering and effective at debugging agent failures may produce lower direct delivery volume than one who writes more code manually, because directing agents well is less visible in commit history than writing code yourself. Pensero's complexity-weighted delivery model captures the value of what was produced after the fact, regardless of whether a human or agent wrote it. The junior engineer whose agent-directed work produces complex, high-quality, low-rework delivery is visible in that metric even if their commit count is low.

Senior and staff engineers are becoming orchestrators and validators, what some in the field are calling "agent conductors." Their value is concentrating in high-level system design, defining the constraints within which agents operate, resolving conflicts between agent outputs that are locally coherent but architecturally inconsistent, and ensuring that the accumulation of agentic decisions over time does not produce a fragmented system.

This is the role that is hardest to measure with traditional metrics. An architect who prevents five architectural drift problems by reviewing agent plans carefully before execution may produce zero direct delivery volume while creating enormous organizational value. The measurement signals that capture this contribution are review patterns, depth and thoroughness of engagement with agent-generated PRs, knowledge distribution (are critical architectural areas becoming more broadly understood or more concentrated?), and the defect rate of code in areas they have touched.

The multi-generational dynamic this creates is one of the more interesting organizational design challenges of agentic adoption. Junior engineers who are AI-native adopt agentic tools faster and develop context engineering skills more quickly. Senior engineers hold the architectural knowledge and governance judgment that prevents agent adoption from producing incoherent systems. Organizations that pair these profiles, juniors teaching AI-native practices, seniors anchoring architectural integrity, are developing a more resilient talent model than those treating agentic adoption as a single-dimension skill.

Is everyone contributing at the level we expect?

This question becomes more complex in agentic environments because "contributing" has multiple dimensions that activity metrics flatten into a single count.

An engineer directing ten agents that each produce high-quality, well-tested, architecturally coherent code, and reviewing their output with genuine depth, is contributing enormously even if their personal commit count is low. An engineer accepting agent output at high volume with shallow review is contributing high activity volume while potentially accumulating quality debt that will surface as rework in subsequent quarters.

The contribution distribution picture in agentic environments requires measurement of both dimensions: what is being produced (complexity-weighted delivery, quality of the resulting code) and what human judgment is being applied to it (review depth, knowledge transfer, architectural consistency). Neither alone is sufficient.

Pensero Calibrate makes this comparison direct. Define cohorts by orchestration effectiveness, engineers whose agent-directed output produces strong complexity-weighted delivery with low rework and broad knowledge distribution versus those whose agent output produces high volume with elevated defect rates and concentrated knowledge. The behavioral differences between these groups are the replicable practices that organizations need to identify and develop.

Did quality improve or degrade as agentic adoption scaled?

This is the governance question underneath the performance question, and it has a specific risk profile in agentic environments.

Architectural drift, the phenomenon where many small, autonomous agent decisions accumulate into a fragmented or inconsistent system, is the quality risk most specific to agentic development. It does not show up as a single high-defect PR. It shows up as a gradual decline in the coherence of the system, visible in rising maintenance costs, increasing incident complexity, and growing time to modify existing components as the codebase becomes harder to reason about holistically.

The measurement signals that surface architectural drift early are knowledge gaps, code areas becoming more concentrated as agents write them without building team understanding, and defect rate trends in the areas of the codebase with the highest agentic contribution. Both are visible in Pensero continuously, not as annual architecture reviews that are already months behind the problem.

Knowledge atrophy at the individual level is the parallel risk. Engineers who stop maintaining deep debugging and implementation skills because agents handle execution may lose the capability to intervene effectively when agents produce incorrect or insecure outputs. This is difficult to measure directly, but it is visible in how the collaboration and review depth signals of senior engineers evolve over time, whether their engagement with the complexity of agent-generated code remains rigorous or becomes increasingly superficial.

What are our best engineers doing differently?

The behavioral profile of effective orchestrators in agentic environments is visible in delivery data, and it is different from the profile of high-performing engineers in traditional development.

Effective orchestrators tend to have: high complexity-weighted delivery sustained at a consistent cadence rather than spiking; defect rates at or below team average despite directing more agentic workflows than peers; broad knowledge distribution across the codebase as they review and understand agent-generated changes rather than approving them without engagement; and collaboration patterns that show active engagement with other engineers' agent-directed work, not just their own.

This is the pattern that separates engineers who are effectively elevating their role through agentic adoption from those who are using agents to inflate output volume without improving the underlying quality of what they deliver. The difference is not visible in commit counts. It is visible in the full picture of what was produced, at what quality, with what downstream rework rate, and with what knowledge transfer to the rest of the team.

As Andrew Eye, CEO of ClosedLoop, described the shift: "At the team level, the only real question is: are we going faster?" The measurement that answers that question honestly in an agentic era is complexity-weighted delivery per headcount, not token consumption, not commit frequency, not the number of agent tasks completed. What shipped, at what complexity, with what quality. That is the signal that tells you whether the agentic adoption is producing genuine acceleration or engineering theater.

Are we getting a good return on what we are investing in agentic development?

The return-on-investment question for agentic development has the same structure as for AI coding tools generally, but the stakes are higher because the investment is larger.

Agentic workflows consume tokens at significantly higher rates than AI-assisted coding. Planning steps, file reads, test execution, iteration loops, each adds to token consumption per task. As engineers shift more work to agentic execution, the cost trajectory compounds in ways that may not be visible until a quarterly finance review.

The delivery lift needs to be proportional. An organization whose agentic adoption is producing 35% higher complexity-weighted delivery per headcount, with stable defect rate and improving knowledge distribution, is generating a strong return. One whose delivery is up 20% but whose rework rate rose 15%, token efficiency degraded 30%, and knowledge gaps are growing, is investing more to get a net negative quality outcome.

Pensero's ROI calculator lets engineering leaders model this calculation against their own headcount and engineering cost, benchmarked against VC and PE portfolio companies running the platform.

Frequently Asked Questions

How is agentic AI changing software engineering roles?

Agentic AI is shifting the engineer's primary contribution from direct code authorship to orchestration and validation. Agents handle implementation, test generation, first-pass review, and maintenance monitoring. Engineers provide the system design intent, the constraints that guide agent behavior, and the judgment that determines whether agent output is architecturally coherent, secure, and aligned with long-term business goals. The role is not being replaced, it is being elevated toward higher-leverage work that requires judgment that agents cannot reliably provide.

What is the key difference between Pensero and LinearB?

LinearB is built around flow optimization - DORA metrics, PR automation, cycle time. Pensero adds complexity-aware performance measurement on top, so teams shipping fewer, harder changes aren't penalized against teams merging high volumes of simple ones.

Can DX show the business value of AI-assisted engineering work?

DX has recently added AI code tracking by commit and PR. Pensero's approach goes a step further by separating human, AI-augmented, and agent-generated contribution, then connecting that to delivery value, defect impact, and cost,  so leaders can see not just how much AI-assisted code shipped, but whether it actually helped.

How is Pensero different from Faros AI?

Faros AI is often associated with broad engineering data infrastructure and integration-heavy analytics. Pensero is more productized for objective performance measurement and leadership decision workflows.

How do you measure engineer performance when agents are doing more of the coding?

By measuring what was delivered and its quality, not who wrote it. Complexity-weighted delivery per engineer per week, with boilerplate and auto-generated code excluded from scoring, captures the value of what the engineer's direction and review produced, regardless of whether a human or agent wrote it. Defect rate, rework patterns, knowledge distribution, and collaboration signals capture the quality and organizational health of that contribution. Pensero measures all of these continuously from actual delivery artifacts, not from self-reports or activity counts.

What happens to junior engineers in an agentic development environment?

The skills that define effective junior contributions are shifting from syntax fluency and boilerplate implementation to context engineering, the ability to give agents the precise technical and business context they need to produce reliable output, and agent output debugging. The barrier to entry is changing from "can you write code" to "can you build systems using AI." This requires rethinking how junior engineers are onboarded, what their ramp-up trajectory looks like, and how their contribution is measured in the first months of their role.

What is architectural drift and why does it matter?

Architectural drift is the gradual fragmentation of system design that occurs when many small, autonomous agent decisions accumulate without sufficient architectural oversight. Each individual agent decision may be locally correct, it solves the immediate problem effectively, while the aggregate effect of many such decisions is an inconsistent, fragmented system that is harder to maintain, harder to understand, and more expensive to modify over time. It is one of the primary quality risks of agentic development at scale, and it requires senior engineers and architects who are actively reviewing agent plans rather than only reviewing agent outputs.

How should performance reviews change in organizations adopting agentic development?

Performance reviews in agentic organizations need to evaluate orchestration quality as a primary dimension alongside delivery volume. This means: how does the complexity-weighted delivery of code this engineer directed compare to peers? What is the defect rate in areas they touched? How deep is their review engagement with agent-generated code? Are they building knowledge that transfers to the team or maintaining isolated understanding? These signals require a measurement framework that captures the full body of engineering work, including review, collaboration, and knowledge distribution, not just direct authorship. Pensero's 11-metric framework, applied at the individual level through Calibrate, provides the evidence base for these conversations.

Does agentic AI create compliance risks for engineering organizations?

Yes, through two mechanisms. The first is audit trail clarity: when agents make changes across many files, the traceability of which agent made what decision, under what direction, with what human authorization, needs to be explicitly managed for regulated industries. The second is R&D attribution: software capitalization and R&D tax treatment require documentation of who worked on what, and when "who" includes autonomous agents, the attribution methodology needs to be clearly defined and defensible. Pensero's artifact-backed attribution connects every delivery item to its contributor context, which supports both compliance documentation and financial attribution in agentic environments.

The information about Section 174/174A in this article is for informational purposes only and should not be construed as tax advice. Tax treatment of R&E costs depends on specific facts and circumstances, industry classification, and company structure. Organizations should consult with qualified tax professionals, CPAs, or tax counsel before making R&E capitalization or expensing decisions. Pensero provides documentation tools to support tax compliance processes, but cannot provide tax advice or guarantee specific tax treatment outcomes.