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UiPath — Engineering Performance

UiPathEngineering capacity
+8engineers above headcount

28 engineers now deliver what 36 would have in Q1 2025.

Effective engineersReal engineers
Apr 2025Dec 2025Sep 2026
Where the work wentBiggest shift: Tests up 7 points
Features30%Maintenance9%Tests41%Docs10%Fixes10%

Performance snapshot

Today's rolling 90-day reading for UiPath, compared with the start of the series. Pick a window to move that comparison point.

Avg. perf / dev / mo

−58.8%

2.90 → 1.19 ETV

Active engineers

+2700.0%

1.0 → 28.0

Features

−34.9pp

64.5% → 29.6%

vs. 500 OSS index

0.31x

3.2x → 0.31x · −69% below

Engineering capacity

Effective engineers behind UiPath, against its pre-AI baseline. Each subject has its own: UiPath's is 0.92 ETV / dev / mo, its first reading in Q1 2025. Per-engineer ETV divided by that gives a capacity multiple, and that multiple applied to the engineers active in the trailing 90 days turns it into engineer-equivalents. The line is the real headcount, so the gap between line and area is what the leverage is worth. Because each baseline is its own, every subject opens at 1.0x on its first day: multiples measure improvement and are not comparable between subjects.

Effective engineers
36engineer-equivalents
+8 engineers above real headcount
Real engineers
28engineers
active in the trailing 90 days
Capacity vs pre-AI
1.3x
per engineer, vs 0.92 pre-AI

UiPath vs. 500 OSS Performance Index

Per-engineer ETV for UiPath against the pooled 500 OSS Performance Index. Both lines are 90-day rolling averages scaled to a 30-day month, so they share one axis and can be read against each other at any point. Pick a window to zoom the chart to it. Latest reading: UiPath is 69% below the index (1.19 vs 3.85 ETV/dev/mo). At the start of the tracked period the gap was 216% above.

UiPath
1.19ETV / dev / mo
+0.12 (+11.2%) past 90d
500 OSS Performance Index
3.85ETV / dev / mo
+1.66 (+75.8%) past 90d

Behind the numbers

Aug

Written summary of the work completed each month.

During August 2026, the team recorded 214 commits across design system stabilization, accessibility enhancements, and test runner upgrades. Output remained steady and in line with the recent run rate, with a Features score of 13 compared to the 10-point 5-month average (+27%). Key efforts focused on refining apollo-wind form controls and updating testing infrastructure via litellm integration.

Highlights

  • Standardized UI panel controls and accessibility states across apollo-wind and apollo-react via e48d6d9b (David), introducing inline validation and ARIA attributes.
  • Integrated DAP variable picker and workflow layout patterns into Storybook templates in 3fae0cf0 (David) and 6f0f7bed (David).
  • Upgraded coder-eval test framework to 0.11.5, routing checker LLMs through litellm with azure/gpt-5.6-luna in 636db813 (Akshaya).

Observations

Based on 214 commits19.2 ETVUpdated Sep 30, 2026, 4:01 PM

Performance Composition

Each month's output split by type of work: Features (new value), Maintenance (sustaining systems), Tests, Docs, and Fixes (rework). The yellow line is output per engineer, so when it rises each engineer is delivering more, whatever the team size did. Unit: Engineering Throughput Value (ETV).

Cost per Performance Unit

+143%

If performance per engineer dropped 59%, each unit of engineering performance now costs approximately 143% more than at the baseline 90-day window (ending 2025-04-01). Treat this as a direction, not a price: the exact figure depends on fully-loaded engineer cost, but which way it moved is not in doubt.

Effective Capacity Lost

−16 engineers

At today's productivity, the current 28-person team delivers the performance equivalent of 12 engineers at the baseline 90-day rolling window (ending 2025-04-01). Per-engineer productivity has dipped below the baseline, leaving the team roughly 16 engineers behind that pace.

CapEx vs OpEx

Each month's work split two ways. CapEx (capitalizable investment) is the work that builds the asset: Features, plus the Tests that prove it works and the Docs that explain it. OpEx (operating expense) is keeping it running: Maintenance and Fixes. The yellow line is the CapEx share, so a rising line means more of the month went into building new rather than sustaining what exists. Unit: Engineering Throughput Value (ETV).

Hours per Repository

Trailing 90-day window (64 working days). Org-level capacity is allocated to each repo by its share of org performance, then split CapEx / OpEx by that repo's own Features + Tests + Docs vs Maintenance + Fixes mix.

uipath-integrations-python
CapEx %
99.2%
OpEx %
0.8%
uipath-langchain-python
CapEx %
83.1%
OpEx %
16.9%
apollo-ui
CapEx %
79.2%
OpEx %
20.8%
uipath-python
CapEx %
76.6%
OpEx %
23.4%
skills
CapEx %
67.1%
OpEx %
32.9%
Total
CapEx %
80.3%
OpEx %
19.7%

Feature Contribution

Who shipped each month's new feature work, as a share of that month's total. Bands of similar width mean new value is coming from across the team; one band that stays wide means most of it rests on the same person. Named engineers shipped the most Features over the period. Everyone else is grouped as Others.

Quarterly Summary

Engineers and the Features / Maintenance / Tests / Docs / Fixes mix for each quarter. Cost / Perf Unit is what one unit of performance costs against Q2'25. Engineers vs headcount is measured against this org's own pre-AI baseline instead, its first reading in the index, so it agrees with the capacity tile and chart above.

Q2'25—
Engineers
7
Cost / Perf Unit
0%
Engineers vs headcount
+3 engineers
Work mix
41% Features
Q3'25+61%
Engineers
15
Cost / Perf Unit
+33%
Engineers vs headcount
0 engineers
Work mix
57% Features
Q4'25+145%
Engineers
27
Cost / Perf Unit
−3%
Engineers vs headcount
+11 engineers
Work mix
41% Features
Q1'26+7%
Engineers
35
Cost / Perf Unit
+18%
Engineers vs headcount
+6 engineers
Work mix
33% Features
Q2'26−2%
Engineers
34
Cost / Perf Unit
+17%
Engineers vs headcount
+6 engineers
Work mix
32% Features