The growth intelligence layer for AI-native engineers

Code generation is becoming commoditized. System-level judgment is not. Wingman helps engineers grow where it matters.

How it works
$ /wingman growth
Your Recent Patterns
Primarily single-module changes
Strong test hygiene
Minimal cross-service exposure
System Signals
Coupling stable in billing service
No boundary violations introduced
No staged rollout patterns yet
Suggested Next Stretch
Lead a cross-service change with a feature flag
Add integration tests + rollback notes

AI tools speed up output.
They don't build judgment.

As agentic AI takes over implementation, the question shifts from "can you build it" to "do you understand the system you're changing."

Metrics distort behavior

PR count, lines of code, and velocity reward output volume — not judgment. Engineers optimize for what's measured, not what matters.

Growth is invisible to managers

Leveling conversations rely on gut feel and anecdote. There's no structured, evidence-based view of how engineers are developing system-level judgment.

AI use obscures judgment

When agents write the code, it's unclear whether engineers are building real understanding or just reviewing output. Growth becomes impossible to observe.


Code generation is cheap.
Judgment is scarce.

What differentiates engineers is not output volume — it's the ability to reason about boundaries, coupling, risk, and system stability. These signals are observable. Growth can be measured without surveillance.


Six signals. Zero surveillance.

Wingman analyzes PR artifacts and produces structured, coaching-oriented feedback. It doesn't score engineers — it surfaces reflection and stretch guidance.

Boundary discipline

Are changes contained to the appropriate module or service layer, or do they leak across ownership boundaries?

Coupling direction

Do new dependencies flow in the right direction, or does the change introduce problematic cross-service coupling?

Safety coverage

Are all modified code paths covered by tests? Does the engineer account for edge cases and failure modes?

Change decomposition

Are changes sized for safe, incremental rollout — or do large PRs bundle unrelated concerns without a staged plan?

Verification behavior

Does the engineer ensure CI passes and review feedback is addressed before merging? Are checks treated as a gate?

Stability outcomes

Do merged changes hold in production? Hotfixes and reverts are a lagging signal of insufficient care at merge time.


Developer-loved first.

Manager value emerges from voluntary usage. The primary success metric is repeat use — not mandated adoption.

"

That's actually accurate. I didn't know that pattern was visible from the outside.

L4 Engineer, pilot user
"

I didn't realize I was avoiding cross-boundary work. Seeing it written out made it click.

L4 Engineer, pilot user
"

I'm going to fix the decomposition issue before merge. I hadn't framed it that way before.

L5 Engineer, pilot user

See the prototype

Experience the Wingman growth intelligence interface firsthand.

WINGMAN DEMO
open_in_new