Why AI Agents Need an Organizational Ontology, Not a Bigger Prompt
How Connectory Genie gives AI agents typed organizational meaning, scoped context, and domain checks before their proposals become software changes.
Metrics that actually predict engineering health, beyond story points and PR counts. Quantifying technical debt, measuring quality, and making the business case for engineering investment.
How Connectory Genie gives AI agents typed organizational meaning, scoped context, and domain checks before their proposals become software changes.
AI writes code faster than teams preserve why systems work. A typed memory framework covering policies, decisions, owners, and open questions across repos.
A practical Connectory use case for biotech and drug discovery teams that need shared org memory, PR evidence, and agent governance across scientific software.
A practical buyer guide for leaders moving from AI coding tools to governed agentic software delivery, with institutional memory as the control layer.
Concrete frameworks for translating technical debt into financial metrics that make CFOs approve remediation budgets instead of asking 'can it wait another quarter?'
AI-generated code ships fast but compounds technical debt silently. Data from GitClear and real incident postmortems reveals the 90-day spike pattern and how to stop it.
Test coverage percentage is a poor predictor of production reliability. Here are the leading indicators—Change Failure Rate, Review Depth Score, and rework rate—that actually tell you whether your codebase is healthy.
Engineering teams track PRs merged and lines written. Almost none track whether AI-generated code survives 90 days in production without incident. Here's what to measure instead.
See how Connectory helps teams tackle these challenges.