Codacy | Blog - Automate your code quality

Your team gave every developer a coding agent, and code now arrives faster than anyone can review it. However, the SDLC around the agent still runs at human speed, so the bottleneck moves to planning, review, and deployment.

When a coding agent breaks a rule your team agreed on, the usual instinct is to give it more instructions, but more material can make the agent worse.Chroma tested 18 models and found performance dropped as input length grew, even when the task stayed the same.

> “What is actually happening right now would absolutely blow your mind and terrify you.” > > > – Steve Yegge

When a coding agent fails, the usual instinct is to swap the model. The evidence points somewhere else. On SWE-bench Pro, Claude Opus 4.5 scored 45.9% in Scale AI’s standardized test harness and 55.4% running inside Claude Code. That’s a 9.5-point difference from the setup alone, when a model upgrade typically moves scores by 2 to 4 points.

The Indonesian financial services company moved its static analysis to Codacy's seat-based platform, cutting direct costs by 18 to 20% and bringing quality gates, AI pull request review and container scanning under one roof.