AI Workflows & Agentic Architecture Directory

OX Alpha beat GPT-5.6 on DeepSWE with 80% Pass@1. This workflow automates stealth model evaluation—benchmarking, red-teaming, and safety scoring—so your team can validate anonymous frontier models before adoption.

An anonymous model scored 80% DeepSWE Pass@1, beating GPT-5.6 Sol by 28 points. The procurement crisis it exposes reveals that enterprise AI safety evaluation hasn't kept pace with model release velocity.

August 2026 shipped 11 models from 5+ providers in 20 days. The safety testing gap is now a production risk—enterprises must automate evaluation or fall behind permanently.

Google shipped Gemini 3.7 Flash just 3 weeks after 3.6 Flash stable—at half the price with 26% better code generation. The $0.75/M token agent workhorse just got smarter.

Gemini 3.7 Flash at $0.75/M tokens delivers 43.6% FrontierCode 1.1 accuracy—matching frontier models at 1/8th the cost. This workflow routes vision and text tasks dynamically, cutting multi-modal agent spend by 60% without quality loss.

Production agents drift silently. NeMo Guardrails embedded as LangGraph middleware intercepts 91% of schema violations and prompt injection attempts before they reach downstream systems—without adding >40ms p99 latency.

MiniMax H3 is the first fully open omni-modal model generating 2K video with native stereo audio. This FastMCP server exposes its video, audio, and image generation capabilities to AI agents for automated media production pipelines.

NeMo Guardrails runs as a standalone Python library. This FastMCP server exposes it as a networked MCP tool, letting any agent validate outputs, detect injections, and enforce schemas without embedding guardrails code locally.

Google's Gemini 3.7 Flash at $0.75/M tokens and Alibaba's Qwen3.8-27B under Apache 2.0 both target the agent workhorse tier. We benchmarked both on 12 production tasks to find the real cost-quality winner.

Deploy a Supabase Edge Functions MCP server that gives AI agents instant serverless backends — sub-50ms cold starts, built-in auth, and real-time database access via Model Context Protocol.

Deploy an autonomous git bisect agent that pinpoints the exact commit causing production regressions using Claude Code for analysis and Linear for issue tracking — reducing MTTR from 4 hours to 12 minutes.

Deploy a prompt cache warming pipeline that pre-computes and semantically deduplicates agent prompts using Redis Cluster — achieving 90%+ cache hit rates and cutting inference costs by 62% across a 200-agent fleet.