Human and Agent Spend | Waydev
Human and Agent Spend

Who did the work: people, AI tools or agents?

Understand how effort and cost split between developers, AI assistants and autonomous agents, workflow by workflow. Then decide where the next AI dollar goes.

Work allocation · this month Sample data
Share of merged work
  • Human58%
  • AI assisted29%
  • Agent authored13%
Workflow Human hours Agent runs AI cost
Code review 420 h 1,180 $6.4K
Test generation 96 h 2,350 $4.1K
Migrations 310 h 640 $5.2K
Signal: Test generation shows the best ratio of agent cost to time saved. Model an expansion.
The Problem

Agents changed who does the work. Your reports did not.

Headcount math is broken

Planning still assumes humans write all the code. Capacity models drift every quarter.

Agent work is invisible

Agent PRs land under service accounts or a developer’s name, so their contribution and cost disappear.

Expansion is guesswork

You cannot tell which workflows deserve more automation without comparing human and agent cost per outcome.

What You Get

A clear split of effort, cost and outcome.

01

Contribution split

Human, AI assisted and agent authored work separated across PRs, commits and reviews.

02

Cost per workflow

Compare human hours and agent cost for review, testing, migrations, refactors and more.

03

Capacity planning

Feed the real mix into Resource Planning so forecasts reflect how your teams work now.

04

Agent attribution

Agent output credited to the agent and its owning team, not hidden under a bot account.

05

Expansion candidates

Waydev ranks workflows where more automation is most likely to pay off.

06

CAFE(S) context quality

Measure whether agents get the context they need, using the CAFE(S) properties.

How It Works

Live in your stack, not another spreadsheet.

1

Connect your stack

Link Git (GitHub, GitLab, Bitbucket, Azure DevOps), Jira, CI/CD and your AI coding tools with read-only access.

2

Waydev maps the work

Every PR, review, ticket and AI session is tied to a team, an initiative and the tool or agent that produced it.

3

Act on Signals

Waydev Agent flags what changed, suggests Goals, and answers follow up questions in plain language.

Waydev reads engineering metadata through read-only APIs. Source code content is never ingested. Visit the Trust Center

FAQ

Questions engineering leaders ask

How does Waydev tell human and agent work apart?
Through commit and PR metadata, bot and agent identities, and usage data from connected AI tools.
Is this used to rate individual developers?
It is built for team and workflow decisions. Role based access controls who sees what.
What is CAFE(S)?
Waydev’s framework for the properties of context agents can actually use. Read more on the Waydev blog.

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