91% of developers use AI tools. Your repo is accumulating technical debt RIGHT NOW.
Software Production Has Outgrown Its Tools.
The flow we built our industry on, idea to editor to commit to pull request to review, was designed for a world where humans wrote every line. That world is over. Developers now orchestrate fleets of AI agents across multiple terminal windows, producing code at industrial scale. The question is no longer whether AI writes your code. The question is: who governs it, who understands the team building it, and who sees the problems before they become crises?
"Version control preserves change, but it does not preserve meaning. Pull requests don't scale when agents work around the clock producing hundreds of variants. The factory floor of software has changed forever. The organizations that thrive will be the ones that can see across this entire system."
From Craft to Industrial Scale
Software production is shifting from individual craft to industrial-scale production. Each era solved the previous era's problems and created new ones.
The DevOps Era
CI/CD, infrastructure as code, and monitoring transformed how software was shipped. The focus was on pipelines and deployment velocity, not code quality or people.
The Platform Era
Internal developer platforms abstracted complexity. Engineering teams got golden paths. But the craft-based production model remained: one developer, one editor, one commit at a time.
The AI Copilot Era
AI started writing code alongside developers. Productivity surged. But so did technical debt, framework reinvention, and AI slop. GitHub became a legacy platform trying to bolt AI onto a 20-year-old workflow.
The AI Agent Era
Developers now orchestrate fleets of agents across multiple terminal windows. Agents produce entire features, review pull requests, and commit code independently. Pull requests don't scale when machines work around the clock. The old SDLC is breaking.
The Engineering Intelligence Era
New infrastructure will reimagine version control, semantic reasoning, and the entire SDLC for AI-native teams. But infrastructure alone is not enough. Organizations need intelligence: visibility into human + AI team dynamics, quality governance at scale, and predictive signals. This is what Connectory is building.
Six Pillars of Engineering Intelligence
The capabilities every engineering organization will need as AI becomes a first-class team member.
Total Visibility
See the whole system, not just the code
Version control preserves change, but not meaning. Connectory goes beyond file diffs to surface repository health, contributor dynamics, AI agent performance, and organizational risk signals in one place.
Quality Governance at Scale
Standards that enforce themselves
When agents work around the clock producing hundreds of code variants, pull requests alone can't keep up. You need governance that matches the speed of production. Codebase-aware reviews, progressive feedback, and automated quality gates that catch slop before it ships.
Human + AI Team Intelligence
Your team is half-human, half-machine. Manage it that way.
The SDLC is being reimagined for a world where machines are primary producers of code. Activity scores, code balance ratios, and burnout risk detection for humans. Cost per commit, effectiveness scoring, and incident rates for AI agents. Same dashboard. Same rigor.
Predictive Engineering Signals
Know problems before they happen
Bus factor risks, burnout signals, flight risk indicators, and commons adoption gaps. Not reports after the fact. Real-time trend detection that lets you act before a crisis forms.
Agent Accountability
AI agents are team members. Hold them to the same standard.
New infrastructure will give agents better tools. But infrastructure alone doesn't tell you which agents deliver ROI and which create technical debt. Compare cost, quality, and reliability across every AI tool in your stack. Promote the best, replace the worst.
Organizational Health as a Metric
Engineering health is a number, and it should go up
A single composite health score for your entire engineering org. Track it over time, benchmark against goals, and understand exactly which levers move it. The organizations that win won't just have better tools. They'll have better visibility.
Infrastructure Is Not Enough
The next wave of dev tools will reimagine version control, CI/CD, and the SDLC for AI-native teams. That's necessary. But better plumbing alone won't tell you if your factory is healthy. That requires intelligence.
| AI-Native Infrastructure | Connectory | |
|---|---|---|
| Focus | Reimagining version control and the SDLC for AI-native workflows | Understanding what matters: code quality, team health, organizational risk |
| Layer | Infrastructure layer: new version control, semantic reasoning, agent tooling | Intelligence layer: signals, governance, and decision-support on top of any infrastructure |
| Question Answered | "How should agents produce and manage code?" | "Is this code good? Is this team healthy? Is this org improving?" |
| Scope | AI agent workflows and code management | Full engineering intelligence: repos, people, agents, and leadership |
| Who It Serves | Developers orchestrating AI agents | Engineering leaders governing organizations at scale |
| AI Agent View | Agent as a primary code producer to be given better tools | Agent as a team member to be evaluated, compared, and held accountable |
| The Gap | Better plumbing doesn't tell you if the factory is healthy | Intelligence on top of any plumbing: bus factor, burnout, quality, and ROI signals |
We don't just watch the machines work. We tell you if the factory is healthy.
The future of engineering is intelligent.
Start with SlopBuster for AI code quality today. The Organization Dashboard is coming soon for teams ready to see everything.