ComputeGuard Atlas

ComputeGuard Atlas is a GPU-aware optimization policy compiler for AI-agent workflows. Modern AI agents often waste compute through oversized retrieval, duplicated context, expensive model choices, and repeated reasoning. Blind optimization can reduce cost, but it can also damage factuality, reasoning quality, or critical verification steps. Atlas solves this by taking an AI-agent execution trace, validating the workflow graph, diagnosing bottlenecks, and compiling a reusable optimization policy. The system applies safe executable agent-layer changes such as retrieval top-k reduction, context compression, Selective Context token optimization, safe model routing, and protection for critical reasoning nodes. The demo shows Atlas loading a realistic deep research trace, validating it, optimizing it, and producing a before/after proof strip. The output includes executable trace changes, token savings, mini runtime simulation, quality gate results, rollback conditions, ROI calculation, and a reusable policy artifact. For hardware relevance, we validated the optimized prompt path on AMD Developer Cloud using ROCm and vLLM. In our benchmark, input tokens were reduced from 139 to 94, a 32.37% reduction. Latency stayed roughly flat in the short benchmark because output generation dominated, but the result proves the token-reduction path runs on real AMD GPU infrastructure. Atlas is not just an observability dashboard. It produces an optimized trace/config, a safety-checked policy artifact, runtime estimates, optional Live AI verification, AMD benchmark proof, and reusable optimization rules for future AI-agent workflows.