Running Codex, Droid, Cline, Goose, and Kilo in Parallel

Most of the time I work with one coding agent at a time, the way most people do. But for the hard questions, the design calls and the gnarly refactors where I genuinely do not know the best path, I have started doing something different. I pose the same question to several terminal agents at once and let them argue it out through their answers. The roster I reach for is codex from OpenAI, droid from Factory, cline, goose from Block, and kilo from Kilo Code. Each ships with a different default model and, just as importantly, a different personality. Running them side by side has changed how I make decisions more than any single one of them has on its own.

Five agents, five temperaments

What surprised me is how much character these tools have, even on similar underlying models. Codex is terse and tends to go straight for the implementation. Droid leans toward structure and wants to understand the whole shape before it commits. Cline talks through its reasoning as it goes. Goose has its own opinions about tooling and how to break a problem down. Kilo brings yet another angle. None of this is in a spec sheet, but it shows up immediately once you put the same prompt in front of all of them.

The differences are not just cosmetic. They come from the default model each agent picks, the system prompting baked in, and the way each one structures its loop of read, think, act. That variety is the entire point. Five subtly different problem-solvers looking at the same question give me a spread of approaches I would never generate by asking one tool five times.

A real workflow, not a benchmark

Here is how it actually goes. I have a design or refactor question I care about getting right. I write it once, carefully, the way I would write it for a senior colleague. Then I hand that same prompt to all five agents at roughly the same time. Crucially, I do not let any of them anywhere near my real repository. Each one runs in its own isolated scratch directory, a throwaway copy or an empty sandbox, so the worst any agent can do is make a mess of a folder I am about to delete.

Then I read all five answers and synthesize. I am not looking for a winner. I am looking for the union of good ideas: the edge case one of them flagged, the cleaner abstraction another proposed, the failure mode a third warned about. The final approach I take is usually a blend, assembled by me, informed by five drafts I did not have to write.

Isolation is not optional

The single most important operational rule is that the agents must not be able to touch the wrong files. Five autonomous tools editing your working tree at once is a recipe for a corrupted state you cannot reason about, where you have no idea which agent wrote which line. So I give each one its own directory and keep my real project out of reach entirely.

This is also why I run them headless, in exec or one-shot invocation mode rather than as interactive chat sessions. I want each agent to take the prompt, do its work in its sandbox, and produce an answer I can read, without me babysitting five terminals. Headless invocation makes the whole thing scriptable and keeps the blast radius contained. The agents are advisors here, not committers. Nothing they produce goes near my codebase until I have read it and decided to write it myself.

Model overrides and why convergence matters

Each agent has a default model, but most of them let you override it, and I use that. Sometimes I want two agents on the same strong model to see how much of the difference is the harness versus the model. Sometimes I deliberately spread them across different models to maximize the diversity of perspectives. There is no fixed recipe. It depends on whether I am trying to stress-test an idea or simply generate the widest possible spread of approaches.

The outcome I have learned to trust most is convergence. When all five agents, on different models and with different temperaments, independently arrive at the same approach, that is a strong signal I am on solid ground. Agreement that survives across that much variety is hard to dismiss. But convergence is not the whole story. Often four of them give me competent, conventional answers and the fifth has the sharpest take, the one insight that reframes the problem. That outlier is frequently the most valuable response in the batch, and I would have missed it entirely if I had only asked one agent.

When an agent falls over

I will be honest about the friction, because it is real. Running five agents across five providers means five places where things can break, and the failures are rarely about the code. The most common one is authentication and account balance. An agent will be running along fine and then drop out mid-task because a provider key expired, a rate limit kicked in, or an account ran out of credit. One reviewer silently vanishes from the council and you only notice when you go to read its answer and there is nothing there.

I have learned to treat this as expected rather than exceptional. If one agent falls over, the other four still gave me a useful spread, so the workflow degrades gracefully rather than failing outright. The juggling of keys and balances across providers is the unglamorous tax on running a multi-agent setup, and it is the part nobody puts in the demo. But the payoff, five independent senior opinions on a hard question in the time it takes one of them to answer, is worth the tax for the decisions that actually matter.