AI Agentic Coding & ReGrade MCP
ReGrade uses NCAST's packet-level comparison to work in-the-loop with your AI coding agent, automatically replaying real traffic against new code and feeding results back so the LLM converges faster and more safely.
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 2 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
By feeding precise difference data back to the LLM, ReGrade reduces wasted tokens on trial-and-error iterations.
Faster iterations
Real traffic replay gives the AI agent concrete signals, shortening the feedback loop from minutes to seconds.
Fewer hallucinations & regressions
Packet-level comparison catches subtle behavioral changes that tests alone miss, keeping the AI honest.
Fewer vulnerabilities
Replaying real traffic exposes security-relevant differences before they reach production.