Solaris

ReceiptIQ — Evidence-Backed AI Software Task Execution AI coding agents can generate code and modify entire repositories, but how can developers know whether their changes are actually correct, safe, and aligned with the original requirements? ReceiptIQ addresses this trust gap by introducing an evidence-backed workflow for AI-assisted software development. A developer provides a coding task, and ReceiptIQ creates a structured implementation plan with explicit objectives, acceptance criteria, allowed files, and restrictions. After human approval, an execution agent works in an isolated workspace, applies the proposed changes, and runs approved tests. An independent verification agent then examines the actual code diff, execution results, test evidence, and original requirements to identify failures, scope violations, or unsupported completion claims. The system combines deterministic checks, AI-powered reasoning, and human approval to make software task execution more transparent and accountable. Our core principle: AI should not just claim that a task is complete. It should provide evidence that allows a human to verify it.