Product engineering health.
Coding, reviewing and product. Three key parts of software development, and one loop. Fix one and the constraint moves to the next.
Trusted by engineering teams
00 · The loop
Three bottlenecks, one loop.
Coding
Speed it up and review becomes the constraint
Reviewing
Speed both up and product feedback becomes the constraint
Product
When specs and tickets stall, coding is starved again
Whatever leaks at the narrowest stage is the constraint. It feeds straight back into coding.
Every developer knows their next step with AI.
The AI adoption framework shows each developer where they already work well with AI and the one next step that unlocks more. Built on evidence from the work itself, not on self-reports.
Five levels, each scored from its own evidence and read separately. There is no combined score, because the lowest row is the one worth working on.
- Agent fluency4.4 of 54.4↑ +0.5Strong habit. Keep AI in the daily loop, 7 of 8 weeks active
- Delegation depth3.9 of 53.9↑ +0.9Hand agents whole tasks, not snippets, on every agent day
- Roadmap alignment3.5 of 53.5→ steadyGrowing. Point more AI hours at roadmap objectives
- Context leverage0.8 of 50.8→ steadyBiggest opportunity. Add a why and outcome to every prompt and ticket
- Agentic autonomy0.5 of 50.5↑ +0.4Ready for more. One agent-owned task a week, end-to-end
Context leverage is the biggest opportunity on this team, at 0.8 of 5 while the rest of the profile is fluent. Every developer gets the same read-out for their own work, so the next step is theirs and not a team-wide mandate.
Every aspect of review, monitored.
Faster coding floods the review queue, so that is where the gain either survives or disappears. Navigara tracks the whole pipeline, and the review agent takes the first pass when a change is too big for a human to read cold.
When a change is too big for the queue, the review agent posts a full first pass: risk map, test gaps, and the three files a human must read.
What you build matters most.
Fast coding against a vague objective is expensive guesswork. Navigara audits every objective, epic and ticket for context health: how much of its context a reader can actually find.
- 2healthy
- 1thin
- 13poor
13 of 16 objectives cannot answer basic questions about why they exist.
- Problem, risk, or opportunityMissing0/15
- Customer or affected partyMissing0/15
- Expected value and reason for the betMissing0/15
- Success measure or evaluation planMissing0/10
- Delivery approach and sub-objectivesIn the record10/10
- Current status and next reviewIn the record5/5
Next step: add problem, risk, or opportunity to the description. 8 of 10 questions have no answer, and the 15 points earned are out of 100.
Context health is scored per objective, and it rolls up to the roadmap so you can see which priorities are being built on a record nobody can read.
See roadmap alignmentHealthy processes are the key to faster delivery.
One agent keeps them healthy. It watches coding, reviewing and product, runs process checks every night, and files a finding the moment something drifts. Error monitoring, but for the way you build.
- Rubber-stamp review on a high-risk changevision · PR #20044 open21 fixed
- Work shipped under an epic the roadmap never trackedENG-288466 open12 fixed
- Team sprint spent on a deprioritized initiativePlatform · sprint 419 open4 fixed
- Commit unrelated to the ticket it referencesvision · PR #211814 open3 fixed
The highest-severity findings from 4 of 6 checks. Each one opens onto the pull request, ticket or sprint it was raised against, so the fix is a click away rather than an investigation.
The full check library, what each one reads, and what a finding looks like when it lands.
See process checksWhat did all this AI cost, and which of it did anyone ask for?
Faster coding, monitored review, healthy specs. Now the bill: $18,240 in 30 days, cut two ways. When fixes and maintenance balloon, the loop is leaking and you are paying the tools to clean up after the tools. Cut by alignment, nearly a third had no stated outcome behind it, which is unhealthy tickets showing up as money.
Fix the specs in part 3 and the unaligned block shrinks. Route each task to the cheapest model that can do it and the whole bar does.
See token spend intelligenceWhat this changes.
Name the stage that is actually your constraint, instead of speeding up the one you can already see
Give every developer one next step with AI, scored from their work and not from a survey
Catch a review queue that is being drained by skipping it, not by clearing it
Stop funding work that has no stated outcome behind it
You can't improve
what you can't see.
See your loop on your own repositories. Read-only setup, under an hour, and the first run is backfilled from the history already in your repositories.
Coding
AI adoption, measured on evidence
Reviewing
Every review tracked, agent on call
Product
Healthy specs before work starts