AI is evolving from coding assistants to agentic systems that autonomously generate, test, and ship code. Your governance must evolve with it — cheer.dev was built for this.
A year ago your team adopted Copilot or Cursor. Six months ago they added Claude, Gemini, or Kiro — and more arrive every quarter. Pull requests are up. Time-to-merge is down. Everyone's faster. But here's what nobody's measuring: which contributions came from an assistant vs. an autonomous agent, whether human review requirements are actually being met, and what happens when agent-authored code is the thing that breaks in production.
Linters check syntax. SAST scanners find vulnerabilities. Code review tools track approvals. None of them distinguish between a developer using Copilot and an autonomous agent that opened a PR on its own. None of them score AI governance alongside security, quality, and hygiene. None of them can tell you whether the human review that happened on an agent-generated PR was thorough — or just a rubber stamp.
The question isn't whether your team uses AI to write code. They already do. The question is whether you have guardrails, circuit breakers, and audit trails that keep pace with AI velocity — or whether you're accumulating invisible risk with every merged PR.
cheer.dev gives you a dedicated Trust category that scores AI governance on the same 0-10 scale as everything else. Not bolted on. Built in from day one.
Every quality tool scores security. Every quality tool scores test coverage. cheer.dev is the only platform with a dedicated Trust category that scores AI governance as a first-class concern.
Trust evaluates human oversight compliance, AI contribution tracking, review thoroughness, and attribution practices. It sits alongside Hygiene, Quality, Security, and Velocity — weighted by your policies, tracked per component, trended across versions.
When your CTO asks "how's our AI governance?" you have a number. When that number drops, you have findings that explain why and tell you how to fix it.
Define what "human oversight" means for your team — and make it policy.
Require a minimum number of human reviewers on AI-generated PRs. Set minimum review time thresholds so rubber-stamp approvals get flagged. Require approved code owners, not just any team member. Configure different requirements per component based on criticality.
These aren't suggestions. They're policy conditions evaluated on every version push, with pass/fail results in your Scorecard findings. When a condition fails, the finding tells your developer exactly what was expected, what actually happened, and what to do next.
You can't govern what you can't see. cheer.dev's Trust Analysis runs on every version push and surfaces AI contribution data alongside your quality metrics — distinguishing between human-assisted AI code and fully autonomous agent contributions.
See AI contribution percentage per component. Track how that ratio changes across versions. Correlate AI contribution trends with quality score trends. Identify components where autonomous agent activity is high but human oversight is low — that's your risk surface.
This isn't surveillance. It's the provenance trail that every compliance framework is starting to require. The same kind of visibility you already have for test coverage and dependency health, applied to the fastest-growing source of code in your organization.
When an auditor asks "how do you govern AI-generated code?" — you show them Activity.
Every Trust Analysis run is logged with timestamp, component, version, and individual check results. Expand any analysis to see exactly which conditions passed and which failed. Filter by type, status, component, or date range. Export for compliance reviews.
This is provenance for the agentic AI era. Not a self-assessment questionnaire — a continuous, automated record of governance in action.
45% of AI-generated code fails security tests.
— Veracode, 2025
Only 33% of developers trust AI-generated code.
— Stack Overflow, 2025
Organizations report 20-40% cost reduction in software development through AI adoption.
— McKinsey, 2025
"Agentic AI demands guardrails, circuit breakers, and comprehensive audit trails."
— CIO.com, 2026
The productivity gains are real. The governance gap is widening. cheer.dev's Trust category closes the gap between AI velocity and human oversight.
See how all the pieces fit together. Quality scoring across Hygiene, Quality, Security, Trust, and Velocity.
Learn about Scorecard →ReleaseQuality gates check Trust scores before components can ship. Governance that flows from measurement to deployment.
Learn about Release →From coding assistants to autonomous agents — quality scoring that builds trust at every phase.