Putnami
AI change throughput

Your agents write faster. Can your review system keep up?

AI increases the volume and speed of proposed changes. It does not increase reviewer capacity, restore missing intent, or explain which evidence makes a change safe.

Discuss one real change

A focused, evidence-led conversation. No platform migration required.

One change, made visible

A reviewer needs more than a plausible diff.

putnami change inspect pr-1842one change · revision-specific
The useful review surface exposes the exact decision, impact, constraints, and evidence attached to the revision.
The problem in practice

Faster generation moves the bottleneck into understanding and review.

Growing queues
More pull requests arrive while the same reviewers remain responsible for impact and risk.
Context reconstruction
Reviewers recover intent, architecture, and ownership before they can judge the change itself.
Rubber-stamp risk
Large or plausible agent output receives shallower review because attention cannot scale with volume.
Correction loops
Missing constraints and late discoveries turn generation speed into rework after review begins.
The question

Can a reviewer understand why an agent-made change is correct without reconstructing the whole system from scratch?

The issue is not whether the agent can produce code. It is whether intent, impact, constraints, and evidence arrive together in a form a responsible reviewer can assess.

One-change test

Follow one agent-assisted change from intention to observed outcome.

Use a recent pull request to measure where human attention is spent and which missing facts create waiting, escalation, or rework.

  • Time spent reconstructing intent, system boundaries, and expected behavior.
  • Review questions that could have been answered by revision-specific evidence.
  • Human interventions required because the agent crossed or could not see a constraint.
  • Correction work discovered after approval, merge, or deployment.
Start a client conversation

Bring one representative change. Leave with a clearer decision.

Leave your work email. Fabien will reply directly to understand the system, select a useful change, and decide whether a bounded engagement makes sense.

No newsletter sequence. No release waitlist. One direct conversation.