AI Agentic Coding & ReGrade MCP
ReGrade replays recorded traffic against the code your agents write and reports what changed. Agents can use the findings to attempt repairs, and your team reviews the result before merge.
The Agentic Coding Loop
- 1Developer (AI/Human) writes new code inside the AI agent platform
- 2AI agent uses ReGrade MCP to replay previous version's recorded traffic to the new version's build
- 3ReGrade records any behavior or performance differences between the two versions
- 4AI agent asks ReGrade MCP for differences and then tags as unintended or intended
- 5Developer (AI/Human) uses differences summary to converge to a solution faster and more securely
- 6AI agent runs tests on new code (ReGrade records all traffic)
Benefits
Less token usage
Behavioral evidence helps agents target repairs. Our benchmark measures LLM API cost per bug fixed; total rollout and operating costs depend on your service.
Faster iterations
Real traffic replay gives the AI agent concrete signals. In our Ghost CMS benchmark, the agent finished 3.2× faster with ReGrade than with raw replay.
Fewer hallucinations & regressions
Field-level comparison catches subtle behavioral changes that tests alone miss.
Fewer vulnerabilities
Replaying real traffic exposes security-relevant differences before they reach production.