AegisCode runs two LLM agents in parallel against every AI-generated commit — detecting injection flaws, hardcoded secrets, and insecure deserialization in real time, then iterates patches until the risk score clears threshold.
Session Tracking
A scan session initialises the moment you start coding. AegisCode monitors file changes in real time, queuing each saved diff for review. Sessions close automatically on inactivity — no manual trigger required.
Dual-Agent Analysis
Two models from different providers run in adversarial mode — one scans for vulnerabilities, the other challenges every finding. Different training data means different blind spots. Together they catch what either alone would miss.
Score-Driven Iteration
The scan report is injected directly into the AI agent's context. Fixes are applied, and AegisCode re-scans. This loop repeats until the risk score clears the configured threshold — or flags for human review.
Track how your codebase security evolves across sessions, agents, and iterations.