Holding NIA's 'AI agents' prediction against six months of reality. The multi-agent standards converged faster than predicted (🔵); the digital employee arrived as a noun but shed the adjective 'autonomous' (🟢). ROI failure rates and the governance gap were nowhere in the forecast (⚪).
A read of DRIFT and TELBench, which drop to the claim level to flag the error spans that affect the answer. Finding harmful spans climbs past halfway, but the first error stalls around 20%. Series Part 4, the finale.
A method-first read of the three papers for evaluating custom agents (MAST, AgentRx, DRBench): a vocabulary for failure, a way to pinpoint where it broke, a testbed shaped like your data. How a builder borrows research-scale work. Series Part 3.
Holding NIA's 'AI agents' prediction against six months of reality. The multi-agent standards converged faster than predicted (🔵); the digital employee arrived as a noun but shed the adjective 'autonomous' (🟢). ROI failure rates and the governance gap were nowhere in the forecast (⚪).
A read of DRIFT and TELBench, which drop to the claim level to flag the error spans that affect the answer. Finding harmful spans climbs past halfway, but the first error stalls around 20%. Series Part 4, the finale.
A method-first read of the three papers for evaluating custom agents (MAST, AgentRx, DRBench): a vocabulary for failure, a way to pinpoint where it broke, a testbed shaped like your data. How a builder borrows research-scale work. Series Part 3.
Starting from the high-score illusion where the top-accuracy model ranks last on utility, this piece lays out why evaluation is moving to process and maps the methods of seven papers. Series Part 1.
Six core patterns of multi-agent workflow (Supervisor / Sequential / Hierarchical / Network / Swarm / Map-Reduce), grounded in primary sources from LangGraph, CrewAI, OpenAI, and Anthropic. Each pattern's topology and fit, plus a decision framework for production.
A phased view of how a Korean financial marketing pipeline (briefing → research → planning → compliance → launch) could be moved onto multi-agents. Phase 1 RAG agent, Phase 2 collaborative multi-agent (CrewAI as one example) with HITL, Phase 3 macro orchestrator + memory layer + observability. Each phase is paired with a component sketch, the patterns worth watching, and where it tends to break - held loosely as one possible path among many.
Anthropic reports 90.2% gains from multi-agent. Cognition publishes 'Don't Build Multi-Agents'. A side-by-side reading of the two camps' primary sources, plus a decision framework for single vs multi.
How do you organize the sliced agents. Four structures, paired with one skeptical paper that argues 'LLM swarms aren't really swarms.' The conclusion lands where it usually does - structure choice gets dragged along by pricing. Part 2 of the series.
The five ways 2026 papers slice an agent, side by side. Two things stand out by the end: Role, Skill, and Judge are different names for the same concept, and the time-axis literature is nearly empty. Part 1 of the series.
44 feature flags, 20 externally inactive. KAIROS, Auto-Dream, UltraPlan, Coordinator, Bridge, Daemon, UDS Inbox, Buddy, plus anti-distillation and undercover mode.