Agents’ Codex

This article compares agent workflow orchestration platforms and explains why the ‘simple’ tool often costs more in governance gaps than it saves in setup time.

State machine patterns give production AI agents the structure to handle multi-step workflows, recover from failures, and maintain context — here’s the architecture that makes it work.

The shift from hand-crafted benchmarks to auto-generated simulation environments is collapsing the cost of agent evaluation — and exposing how far even the strongest models still lag behind humans.

Autonomous agents introduce attack surfaces traditional security never anticipated — and the new OWASP ASI framework is the first standard built to address them.

KV cache memory kills agent throughput at scale — here’s how to fix it with TurboQuant, FP8 quantization, and H2O eviction in production.

Multi-agent FinOps systems don’t just surface waste—they eliminate it automatically, and the numbers prove it.

The TCO math has shifted decisively toward RAG for most enterprise agents — unless your query volume exceeds 100K/day with static knowledge.

gstack packages 21 Claude Code role configurations as SKILL.md files — and that’s both its strength and its limit.

Sparse MoE architectures have won the LLM scaling race — here is how to actually run them at production scale.

OpenAI’s Computer-Using Agent (CUA) navigates any website by seeing and reasoning — no DOM, no selectors. This deep dive covers how CUA works, how it compares to Anthropic’s approach and traditional RPA, and where the technology still falls short.