Coding harnesses converged on script-orchestrated subagent fleets with adversarial self-verification

The dominant orchestration shape is now a deterministic script that fans work across many subagents, has independent agents attack a problem from different angles, then has other agents try to refute the findings until answers converge before anything reaches you. Anthropic laid it out with dynamic workflows in Claude Code. Source.

The convergence is the story. Claude Code's dynamic workflows run JS scripts spawning tens to hundreds of agents. Google Antigravity's "manager" delegates task clusters. Codex added thread-level delegation controls. Microsoft's Nadella demoed a "Chain of Debate" multi-agent system to engineers the same week. Source. Four vendors, four names, one architecture. The differentiator stopped being "get a good suggestion" and became "delegate a task cluster and verify it."

The verify half is what most people are sleeping on. Fanning out is easy and everyone figured it out a year ago. The new part is the adversarial refutation pass: after N agents find things, you spin up skeptics whose whole job is to disprove each finding, and you only surface what survives. This is why the current generation of harnesses hallucinates less. It's not a better base model. It's that a plausible-but-wrong finding gets killed by three agents told to refute it before you ever see it. arXiv even dropped a paper this week arguing verification is a distinct scaling axis, that spending compute on a stronger verifier can beat scaling the generator. Source.

Pair this with the previous story and you get the full picture. The community named the loop; the vendors shipped the fan-out-and-refute engine underneath it. If you run Claude Code, v2.1.202 added a "Dynamic workflow size" setting (small/medium/large) that caps how many subagents a workflow spawns, which is your cost lever. Source. Codex added rollout token budgets that abort a turn when exhausted. Source.

The actionable structure: stop asking one agent to do a hard task well. Instead, fan the task across a few agents with different framings, collect their outputs, then dispatch a separate wave whose only instruction is to refute. Keep the loop running until the survivors stop changing. I've started doing this by hand for anything where being wrong is expensive, and the false-positive rate on my own reviews dropped noticeably. The catch nobody advertises: this burns tokens fast, and without a budget cap a fan-out can quietly run up a bill. Set the ceiling before you set it loose.