Blitz TraceGrid

Blitz TraceGrid is visual execution intelligence for AI-generated software. The problem is simple: AI can create code faster than teams can understand its behavior. Developers and security reviewers still need to know what happens after a user action, where trust boundaries appear, which path deserves investigation, and whether the visible behavior is well-supported or just a weak inference.

TraceGrid starts from uploaded source files, a ZIP archive, a repository path, public GitHub URL, or deterministic demo fixture. It extracts AST-lite evidence from Python and TypeScript/JavaScript, reconstructs a causal graph across UI, API, backend, security, dependency, and data layers, then lets the user choose a Trace Target. That target becomes a focused trace slice with incoming context, outgoing branches, and one primary replay path selected for animation.

The new Behavioral Trust Map scores AI-generated behavior without adding more chatbot layers. It highlights behavior confidence, unknown zones, architecture drift, execution entropy, security consequence hotspots, and hallucination-like signals that need confirmation. In plain language, it asks: can we trust this path yet, or do we need runtime proof?

The flagship Run AI Investigation flow makes the app agentic. TraceGrid scores graph targets by risk signal, connectivity, and layer priority, selects a high-value investigation root, traces it, runs Architecture, Security, Execution, and Explainer agents, and outputs root cause, attack surface, replay path, recommended fix, confidence, and a plain-English explanation.

TraceGrid is deployed as a production-shaped web app with Next.js, FastAPI, Docker, Nginx, Vultr, Featherless inference, and Speechmatics voice control. Current build: static causal reconstruction, not full runtime telemetry. The roadmap is OpenTelemetry, browser/backend hooks, eBPF event capture, taint/data-flow analysis, causal diffing, and deterministic replay.

Note: the demo URL is substituted with the GitHub repository link.