Every engineering leader has watched a single LLM sprint through a feature, only to hand humans an "almost right" diff that still needs surgery. You can tweak prompts forever or upgrade to the latest giant model, but one agent playing all roles hits a ceiling. The breakthrough comes when you treat LLMs like a council of specialists instead of a lone genius. Humans have trusted panels of experts for centuries because diverse perspectives expose blind spots. The same thing happens with agents. Even Andrej Karpathy's recent llm-council experiment (https://x.com/karpathy/status/1992381094667411768) showed that models eagerly critique one another, vote on the best reasoning, and ship stronger answers than any solo run. Coding workflows work the exact same way: a coordinated team of agents routinely beats the best single model left to improvise. The more your process resembles a deliberate committee—agenda, roles, checkpoints—the more those agents behave like seasoned teammates instead of creative freelancers.
An individual agent is relentless at execution but brittle at self-reflection. It suffers the same failure modes we see in every single-agent postmortem:
Throwing more tokens or temperature tweaks at those issues only slows the run down. What fixes them is a second (and third, and fourth) perspective interrogating the work while it's happening. Multi-agent work feels less like asking the same person to proofread their novel and more like passing a manuscript through editors, copy chiefs, and fact-checkers who notice different flaws.
A council works because expertise is distributed. In a healthy human review, the architect, the implementer, the QA lead, and the operator all look at the same plan through different lenses. When agents take on similar roles, you get the same compounding effect. Each role keeps the others honest and creates natural friction that flushes out assumptions before they hit production:
Five roles is illustrative, not prescriptive. The point is that each role enforces a different responsibility, and that diversity can include different model families. We routinely pair a GPT implementer with a Claude reviewer precisely because their reasoning styles differ. One sees structure others miss, another is relentless at spotting inconsistent tone, and a third can summarize trade-offs for humans. Like Karpathy's council, the agents often prefer a peer's answer over their own—and that's the behavior we want inside production workflows. You start to see runs in which every stage contains its own reviewer and you can trace exactly who caught which bug.
A mistake people make is hiring multiple agents but letting them operate sequentially with no dialogue. Councils work because the members talk to each other in real time, challenge premature conclusions, and share intermediate artifacts. In AI workflows that means collaboration is woven into every stage, not bolted on at the end:
Humans now consume a stream of artifacts: spec deltas, reviewer commentary, verifier logs. Instead of parachuting in at the end, they skim the council's debate, understand why decisions changed mid-run, and decide whether to sign off. Every artifact ties back to a role so ownership is obvious.
In Karpathy's llm-council, every model ranks its peers and a chairman synthesizes the final answer. The same pattern applies to code. Verification is the chairman. The run only finishes when the verifier agent confirms the RED/GREEN/VERIFY loop passed and the spec promises hold. That agent can even sit on a different model to maximize diversity or run purpose-built tooling like linters and integration suites. When verification fails, the fixer steps in with targeted edits, the verifier rechecks, and the loop continues until the chairman votes yes. No amount of prompt cleverness can replicate that kind of forced accountability in a single-agent run, because a lone model always lets itself off the hook once it sees a green test.
Councils descend into chaos without process. Zenflow's orchestration layer exists so multi-agent runs feel like a disciplined assembly line, not a brainstorming session. The workflow provides the agenda and the turn-taking rules that let the council function:
With orchestration, humans review the synthesis of multiple viewpoints instead of wrangling raw agent output. They see when the planner overruled the builder, when the reviewer insisted on an invariant, and when verification flagged a test gap. The council does the arguing; humans make the decision.
Key takeaway: Councils of agents beat solo LLMs when they bring diverse perspectives, collaborate during every stage, and operate inside a deterministic workflow like Zenflow.