Agentic Engineering: Know What's Human, Know What's Machine

Your developers are using AI coding tools. Some of them heavily. The commits look the same in Git — there's no flag that says "a machine wrote this." But the implications for your org are massive. Headcount planning, performance evaluation, code quality assessment — all of it changes when 40% of your output is agent-generated.

Most engineering leaders have no idea what percentage of their codebase is AI-assisted. They can't answer whether AI-generated code has more bugs, whether certain teams adopt AI faster, or whether their investment in AI tooling is actually paying off. It's a blind spot at exactly the moment when visibility matters most.

The teams that figure out human-agent collaboration first will ship 3-5x faster. The ones that don't will keep measuring with tools built for a world where every line was written by hand.

How InteliG Solves This

• → Detect AI-assisted contributions automatically. InteliG analyzes commit patterns, authorship signals, and code characteristics to identify which contributions are AI-assisted. No developer self-reporting required.
• → Track AI adoption by team and individual. See which teams embrace AI tooling and which don't. Understand adoption curves, identify champions, and find teams that need support or training.
• → Compare quality across human and agent code. Do AI-assisted commits have more reverts? Higher review rejection rates? InteliG measures quality signals across both, so you know if your agents are shipping production-grade code.
• → Plan headcount with real data. If AI handles 35% of your feature code, that changes your hiring plan. InteliG gives you the numbers to make headcount decisions based on actual output, not assumptions.

Questions You Can Ask Cognis