LaunchShield Swarm

LaunchShield Swarm is an AI security audit demo built around one core idea: agent work should be priced and settled one tool call at a time. A single audit is already a swarm of tiny actions such as reading repository files, checking dependencies, probing a live site, asking an LLM for deeper review, verifying high-risk issues, and generating fix suggestions. Our app breaks that workflow into atomic, paid invocations and turns the execution feed into a transparent billing and evidence trail. Every tool call has a target, a price, a result summary, and a settlement record, so reviewers can see what happened, why it mattered, and how much it cost. The product runs as a FastAPI web app with live SSE updates, a billing waterfall, finding cards, and a profitability matrix. In mock-first mode the whole flow works end to end with zero credentials, which makes the demo easy to run. In real mode the same architecture can use Arc testnet payments, Circle-linked USDC settlement, GitHub-backed repository fetches, CDP-backed browser probes, OpenAI-compatible LLM analysis, and AIsa verification. We also support a full repository scan mode and clearly label mock versus real providers in the UI. The reason this project belongs in the Arc hackathon is economic as much as technical. High-frequency AI security work creates many low-value but high-signal actions. Traditional gas-heavy settlement destroys the margin. Arc plus stable-value micropayments make it practical to bill each action fairly, preserve provenance at the tool level, and support agentic security workflows that scale beyond flat subscriptions.