Human and Agent Spend

See how
engineers and AI
coding agents
build and
ship together.

See where Cursor and Claude-Code speed up your
engineers, where they add review and rework, and
where usage should scale or be managed.

Book a demo
Human and Agent Spend Map
live
Cursor Claude-Code Copilot
ENGINEER
184h
prompts and reviews
AI CODING
$6,100
Cursor and Claude-Code
Shared delivery cycle
Plan
Build
Review
Merge
Deploy
Improve
Collaboration profile
Checkout Redesign
Efficiency improving
Human effort
0h
AI tool cost
0
Code
0%
Expand
Test creation
Manage
Backend prompting
The shift

AI spend is now embedded in how engineers code with AI

Engineers prompt, AI suggests, engineers review and accept. Yet most companies still measure AI as a flat tool costs.

Is AI making the team more efficient, or just moving cost into review, rework, and risk?

Fragmented today, converged in Milestone
AI tool usage
Prompts and accepts
Pull requests
Review queue
One Spend Map
Collaboration Spend Map

See the full collaboration
pattern.

One operating view across every feature, team, and delivery stage.

Human and Agent Spend Map
Q2 · all teams
Cost signals
Human effort 184h $26,680
AI tool cost$6,100
Review effort 38h $5,510
Rework effort 11h $1,595
Full cycle costengineer + AI
$0$33,785 time + $6,100 AI
Plan Build Review Merge Deploy Improve DELIVERY CYCLE Checkout

Engineer and AI coding signals merge into one cycle

Expand
Test creationscale
Documentationscale
Routine refactorsscale
Manage
Backend promptinglimit
Security codelimit
Repeated promptslimit
Capabilities

Finally know how much
each feature actually
costs.

Cost per feature

Cost per feature

The full price behind every feature: engineer time, AI tool usage, model cost, review, and rework in one number.
Collaboration modes

Collaboration modes

See how each feature was built: human written, AI assisted, AI generated, or reworked.
Expansion signals

Expansion signals

Which features AI coding ships faster, and at what quality and cost.
Control signals

Control signals

Where AI generated code needs review, guardrails, or guidance.
Code based intelligence

Code based intelligence

Git data, pull requests, reviews, and fixes price what each feature truly took.
Feature and team views

Feature and team views

Compare spend and collaboration by feature, product area, and team.
Decision intelligence

Turn collaboration
data into decisions.

Milestone surfaces the few moves that matter. Open one to see the signal and the recommended action.

Expand AI is speeding up repetitive frontend work

Engineers using Cursor and Claude-Code ship repetitive UI changes faster, with low review burden and no rise in post release fixes.

Recommended action
Encourage AI assisted UI scaffolding across similar frontend work.
Manage Heavy backend AI use needs guardrails

Using Cursor and Claude-Code on backend code creates repeated pull request revisions and more review time.

Recommended action
Add a review step before AI generated changes touch shared logic.
Improve One team leans on AI a lot but ships very little

High AI tool usage, but low accepted code and high rework from generated changes.

Recommended action
Coach the team on prompt patterns and lower cost model choices.
Protect Keep humans leading architecture

Architecture changes still land better when engineers lead and use AI for support.

Recommended action
Use Cursor and Claude-Code for tests, docs, and refactors, not core design.
Autonomous agents

When agents run on their
own, watch the loops.

Some work runs with no developer in the seat. Milestone tracks these task agents on their own, so runs, retries, and loops never become hidden costs.

Background runs
Retry loops
Approval gates
Autonomous task agents running
Runs
0
Avg retries
0
Needs approval
0
Nightly refactor 4 retries $42 review
Dependency upgrade 1 run $8 ok
Test backfill 2 retries $19 ok
Why Milestone

Milestone connects AI data to your code and to every feature.

Usage data alone cannot tell you what a feature costs. Milestone joins AI activity to Git history and ties both to the features your teams ship.

AI data
Cursor and Claude-Code
Tool usage and accepts
Tokens and model cost
AI generated changes
Git code
Commits and reviews
Commits and pull requests
Review and rework
Post release fixes
Features
Cost and value
Cost per feature
Value delivered
Spend efficiency
Only by connecting all three can you answer the question vendors cannot: how much did this feature cost?
Built for

Built for the shift to human and agent engineering.

CTOs and VPs
CTOs and VPs of Engineering
Are you AI driven, or just AI assisted?
AI coverageshare of work touched by AI
Coding64%
Review29%
Testing71%
AI coverage by activityDriven vs assisted
Engineering Managers
Engineering Managers
Protect team focus and review load.
Review loadDelivery speed
AI Platform Leaders
AI Platform Leaders
Decide what to scale and what to govern.
72%
Expand
28%
Manage
Workflow policyExpansion zones
Finance and Ops
Finance and Operations
Tie AI spend to delivery and value.
3.2×value per dollar
Cost per featureMeasurable ROI
Product Leaders
Product Leaders
See the investment behind every feature.
Checkout84%
Search61%
Billing43%
Feature spendRoadmap value
CTOs and VPs of Engineering
Are you AI driven, or just AI assisted?
AI coverageshare of work touched by AI
Coding64%
Review29%
Testing71%
AI coverage by activityDriven vs assisted
Engineering Managers
Protect team focus and review load.
Review loadDelivery speed
AI Platform Leaders
Decide what to scale and what to govern.
72%
Expand
28%
Manage
Workflow policyExpansion zones
Finance and Operations
Tie AI spend to delivery and value.
3.2×value per dollar
Cost per featureMeasurable ROI
Product Leaders
See the investment behind every feature.
Checkout84%
Search61%
Billing43%
Feature spendRoadmap value

AI native teams need collaboration visibility.

Expand AI where it creates value. Manage it where it creates hidden costs.

Book a demo AI Governance & Limits →