Agent Evaluation, Red-Teaming, and Benchmarking Infrastructure

This circuit begins one level above the isolated tooling that currently evaluates AI agents. It maps the loop where capability measurement, safety validation, and hardware constraints converge into a single deployment gate.

The pattern stabilizes across seven distinct signals. DeepSWE establishes a standardized surface for measuring long-horizon trajectory planning and multi-file codebase editing. RAMPART translates adversarial safety scenarios into deterministic pytest checks that run as continuous integration thresholds. The Agent Sandbox Taxonomy formalizes the isolation boundaries. It maps defense layers against threat categories to quantify containment. Raindrops closes the visibility gap. It enables local-first state inspection and execution tracing without external telemetry. WhichLLM and WhatCanIRun anchor the loop to physical constraints. They rank open-weight models against actual hardware specifications to prevent deployment friction. promptfoo binds these layers together. It provides a declarative evaluation syntax that integrates directly into CI/CD pipelines.

This circuit resists the fragmentation of black-box scoring. It avoids the failure mode where agents pass isolated benchmarks but collapse under stateful workloads. It rejects vendor-locked evaluation metrics that obscure cross-platform performance. The loop forces every capability claim to pass through reproducible validation.

Measurement becomes a continuous property of the agent lifecycle. Benchmarks feed into sandbox taxonomies. Sandbox scores inform hardware routing. Hardware constraints dictate model selection. Model selection feeds back into adversarial testing. The infrastructure operates as a closed loop of verification.

The circuit is complete when a single declarative test configuration can validate trajectory planning, enforce safety thresholds, verify sandbox isolation, and confirm hardware compatibility across all supported runtimes before deployment.