EngineeringTwin
Digital Authority for engineering
AI can count your tokens and PRs. Does it know what “productive” means?
AI coding spend can rise long before trusted delivery does. Connecting Claude Code, Cursor, GitHub and Jira gives AI activity data, not task complexity, accepted output, rework, regressions or cost per accepted result.
EngineeringTwin shows which agent workflows create trusted acceleration, and which are only generating more activity.
MCP connects AI to your coding agents and repositories. EngineeringTwin counts only what you accepted, and what it cost to get it.
AI + MCP
Access without business meaning
PR volume +140%
All generated PRs · inferred from connected activity
- Generated PRs counted as delivered output
- Every task treated as equally difficult
- Rework, reverts and regressions ignored
EngineeringTwin
Access with business meaning
Accepted output +18%
AI spend +160% · Cost per accepted result +34%
- Only accepted and shipped work included
- Weighted using your task-complexity classes
- Rework, reverts and regressions applied
Intervene now
Review where AI spend rose 160% without accepted output before the next budget cycle.
Reads fromClaude Code · Cursor · GitHub Copilot · Jira · and 750 more
The agentic engineering scorecard
More AI activity is not productivity. Measure what survives.
Inspired by Bessemer’s agentic engineering measurement framework, EngineeringTwin shows whether AI activity becomes trusted output, real productivity and economic value, using your definitions of complexity, acceptance, quality and cost.
| Metric | How it is calculated | Why it matters |
|---|---|---|
| Usage Agentic compute intensity |
Token intensity =
total AI tokens / workflow / period
Spend intensity =
API-equivalent AI cost / workflow / period
Measurement window: rolling 30 days. Always segment by team, workflow, task class and model. |
Shows where agentic compute is being used and where costs are accumulating. Where is our AI investment going? |
| Quality Trusted output |
First-review acceptance =
AI-touched PRs accepted without rework
÷ AI-touched PRs reviewed
14-day revert rate =
AI-touched PRs reverted within 14 days
÷ AI-touched PRs merged
Measurement window: acceptance weekly and rolling 30 days. Reverts use PRs with a complete 14-day post-merge observation window. A high first-review acceptance rate combined with a low revert rate is the strongest operational signal that agent output can be trusted. |
Determines whether generated work survives review and continues to hold up after it is merged. Can we safely trust and scale the output? |
| Autonomy One-shot rate by task complexity |
One-shot rate at complexity Tn =
tasks accepted without human correction
÷ attempted tasks at complexity Tn
Measurement window: rolling 30 days, calculated separately for each task-complexity class. Calculate the rate separately for each task-complexity class. Never combine routine maintenance and architectural work into one autonomy percentage. |
Identifies the boundary at which agents can work independently and where human judgment is still required. Which work can we delegate safely? |
| Productivity Complexity-weighted accepted throughput |
Complexity-weighted accepted throughput =
sum of complexity points for accepted work
÷ time period
Σ accepted complexity points / time
Measurement window: weekly, shown with a rolling four-week trend. Use the company’s existing task taxonomy or complexity model. EngineeringTwin must not impose a universal complexity score.T0 — ConfigurationT2 — Standard bug fixT4 — Complex featureT7 — Architectural change
|
Prevents large volumes of trivial work from appearing more productive than smaller volumes of difficult, valuable work. Is more difficult, trusted work reaching production faster? |
| Economics Cost per accepted result |
Cost per accepted result =
AI cost + allocated human cost
÷ accepted production outcomes
Complexity-weighted throughput per total cost =
accepted complexity points
÷ total human + AI cost
Measurement window: rolling 30 days, with a quarterly trend for investment and budget decisions. Companies can begin with AI cost only and add allocated human cost when that data becomes available. |
Shows whether the productivity improvement is economically sustainable and where model routing or workflow changes are required. Is this workflow worth scaling? |
Informed by: Bessemer Venture Partners, The Agentic Awakening: Playbook, “Measurement,” pp. 12–17, 2026. Metrics operationalised by HelloTwin.
Example using demo data
More activity.Much less productivity gain.
The same agent workflow can look highly productive when measured by activity, and uneconomical when acceptance, quality, complexity and cost are applied.
AI activity
- PR volume
- +140%Activity increasedAll generated pull requests
- AI spend
- +160%Cost increasedTokens, subscriptions and API-equivalent cost
Trusted output
- First-review acceptance
- 62%Against your thresholdAccepted without human rework
- 14-day revert rate
- 9%Against your thresholdMerged work subsequently rolled back
- One-shot rate
- 58%Against your thresholdStandard bug fixes accepted without correction
Governed result
- Complexity-weighted accepted throughput
- +18%Trusted throughput increasedAccepted work adjusted for task difficulty
- Cost per accepted result
- +34%Cost increasedTotal AI and allocated human cost
Comparison period: last 30 days versus previous 30 days. Revert rate includes only PRs with a complete 14-day observation window.
PR activity +140%
Trusted productivity +18%
Cost per accepted result +34%
More generated work did not translate into equivalent trusted productivity. AI spend grew faster than complexity-weighted accepted output.
What the numbers mean
AI generated substantially more pull requests, but acceptance, rework and task complexity reduced the actual productivity gain to 18%. Because spend increased faster than accepted output, each trusted result became 34% more expensive.
Engineering decision
Do not scale every agent workflow yet.Scale workflows with high acceptance and low rework. Reroute routine work to lower-cost models. Fix or stop workflows with high correction, revert or regression rates.
Scale · High acceptance
Reroute · Good quality, excessive model cost
Fix or stop · High rework or revert rate
The AI-era engineering north star
Trusted, complexity-weighted outcomesper total human + AI cost.
Σ complexity points of accepted outcomes
total human cost + total AI cost
More tokens, more code and more PRs are inputs. The outcome is accepted, production-ready work, adjusted for complexity, quality and total cost.
EngineeringTwin prepares the evidence before the investment decision. Your leaders decide what to scale, reroute or stop.
HelloTwin doesn’t replace rigor with AI, it combines them. The AI reasons on top of a governed digital twin, working from numbers that were computed correctly, not guessed. That’s the only architecture I’d put in front of a decision maker.
Better together
Engineering productivity doesn’t live in GitHub.Every function determines whether the output has value.
Engineering shows what was delivered. Product provides priority, expected outcome and task complexity. Finance provides human and AI cost. Success shows production and customer impact. Together, the Twins create one governed view of agentic engineering performance.
EngineeringTwin
One governed view of AI usage, quality, accepted throughput and cost
One Semantic Digital Twin
Same work · Same teams · Same products · Same costs · Same terms · Same metrics · Goals that roll up
More code is not the goal. Trusted outcomes per total cost are.