Is there any point using more than one coding agent?

Yes, if you exploit the difference between them. Agents trained differently have different failure signatures, so one reviewing another's diff catches issues the author agent defends. Running two agents on the same task in parallel is much weaker, since you mostly get two similar answers and a tie to break.

How should I split work between coding agents?

By stage, not by task. One agent plans and writes a plan file listing the files to touch, the approach, and the proving tests. Another implements against that file. A third, or the planner, reviews the diff against the plan. Each boundary produces an artifact in the repo instead of a conversation.

Why does the handoff between agents need to be a file?

Because sessions do not transfer between tools. A file does. The implementing agent needs the plan, not a transcript of how the plan was reached, and a plan committed to the repo can be diffed against what was actually built, audited later, and read by any agent you adopt next without a migration.

How do I stop maintaining separate config files per agent?

Move project knowledge out of tool configuration and into a searchable index. RDK stores your code, docs, and decisions as encrypted private chunks any agent can query through a retrieval tool. You record a convention once and every agent gets it, instead of three config files drifting apart at three different rates.

Does running multiple agents cost more in tokens?

It does if each one relearns your project every session, which is the usual setup. With a shared retrieval layer the agents pull the relevant chunk instead of rediscovering your architecture. Stacked retrieval keeps 40 to 65 percent of queries in your private index and another 15 to 20 percent in the public network, so token spend drops 80 to 90 percent.

How to Combine Multiple AI Coding Agents Into One Workflow

Assign each agent a stage rather than running them on the same task. Use one to plan, another to implement, and a third to review, since agents trained differently catch different mistakes. The hard part is keeping context consistent across tools, which a shared, vendor-neutral knowledge layer solves.

Why one agent for everything is a weak default

Coding agents are not interchangeable. They differ in how aggressively they refactor, how much they explain before acting, how they handle an ambiguous instruction, and where they quietly get things wrong. Those differences come from different training and different harness design, and they show up as distinct failure signatures.

Running a second agent is only worth the friction if you use it against those differences. Two agents doing the same task in parallel mostly gives you two similar answers and a tie to break. One agent checking another's work gives you something you cannot get from a single tool: an outside reading of the same code by a model with no memory of writing it.

So the question is not which agent is best. It is which stage of your workflow each one is best at, and whether the handoff between them costs less than the benefit.

Assign stages, not tasks

The clean split is plan, implement, review, and each boundary should produce a file rather than a conversation.

Planning is where you want an agent that reads widely and commits to nothing. Its output is a written plan: the files it expects to touch, the approach, the tradeoffs it considered, and the tests that will prove the change works. That plan lands in the repo as a plain markdown file.

Implementation is where you want an agent that works well in a tight edit and test loop and stays inside the stated scope. It reads the plan file, not a transcript of how the plan was reached. This is what makes the handoff work across tools: the second agent does not need the first agent's session, it needs the artifact.

Review is where you deliberately switch models. The reviewing agent gets the diff and the plan and answers one question: does this change do what the plan said, and what breaks. Its output is also a file, so the implementing agent can act on it without you playing courier.

Why cross-model review finds more

An agent reviewing its own diff has already justified every line of it. It reads its own reasoning as context and tends to confirm the approach rather than question it. A different model reading the same diff has no such attachment, and it has different blind spots, so the overlap of what both miss is smaller than what either misses alone. This is the same reason a second pair of human eyes catches bugs the author has read past ten times.

Keep the artifacts in the repo

Plan files, review notes, and decision records belong in version control next to the code they describe. That makes the handoff auditable, lets you diff a plan against what was actually built, and means any agent joining later can read the history without needing anyone's session transcript. If your multi-agent workflow only exists in chat windows, it does not survive the week.

The tax nobody mentions: configuration drift

Every coding agent wants to be told about your project, and every one of them wants it in its own file format. So you write your conventions once for the first tool, copy them for the second, adapt them for the third, and now three files describe the same standards.

They drift immediately. You update the testing convention in one and forget the others. One still references a directory you renamed last month. A rule you tightened after an incident exists in one file and not the rest. The result is agents that disagree about your own standards, and a review agent flagging code as wrong that the implementation agent was correctly told to write.

The deeper problem is that all of this knowledge is trapped per-tool. Each agent starts blind, learns your project during a session, and forgets when the session ends. Add a second agent and you pay that learning cost twice. Add a third and you pay it three times, in tokens, every day.

One knowledge layer, many agents

The fix is to stop treating project knowledge as tool configuration and start treating it as a searchable store that lives outside every agent.

RDK indexes your code, docs, and notes from your local vaults as encrypted private chunks on the RDK network. Any agent that can call a retrieval tool queries the same index. Claude Code retrieves it. Codex retrieves it. The next tool you adopt retrieves it, without a migration, because the knowledge was never stored inside a tool in the first place.

That changes what happens when you learn something. Record the decision once and every agent has it on the next query, instead of three config files diverging quietly. It also removes the duplicated learning cost: agents retrieve the relevant chunk instead of rediscovering your architecture from scratch each session.

The economics follow the same shape as any retrieval setup. A private index answers 40 to 65 percent of queries, the public network of published chunks adds another 15 to 20 percent, and the LLM handles the remaining 5 to 10 percent as fallback, so token spend drops 80 to 90 percent. In a multi-agent setup that saving multiplies, because you were previously paying every agent to learn the same things separately.

A division of labor worth starting with

Start with two agents and one handoff. Adding a third tool before the first handoff is clean just multiplies the coordination cost.

A setup that holds up in practice: one agent plans and writes the plan file, a second implements against that plan and runs the tests, and the planning agent comes back for review because it knows the intent and did not write the code. Everything crosses the boundary as a file in the repo. Every agent queries the same retrieval layer for conventions and prior decisions.

Run it on a real change before you invest in tooling around it. If the handoff files are useful to you as a human reader, the workflow will hold. If they are noise you skim past, you have built ceremony rather than leverage, and you should go back to one agent doing the whole job.

The goal is not the maximum number of agents. It is a setup where each stage is handled by the tool best suited to it, and where teaching any of them something teaches all of them.

Frequently asked questions

Is there any point using more than one coding agent?: Yes, if you exploit the difference between them. Agents trained differently have different failure signatures, so one reviewing another's diff catches issues the author agent defends. Running two agents on the same task in parallel is much weaker, since you mostly get two similar answers and a tie to break.
How should I split work between coding agents?: By stage, not by task. One agent plans and writes a plan file listing the files to touch, the approach, and the proving tests. Another implements against that file. A third, or the planner, reviews the diff against the plan. Each boundary produces an artifact in the repo instead of a conversation.
Why does the handoff between agents need to be a file?: Because sessions do not transfer between tools. A file does. The implementing agent needs the plan, not a transcript of how the plan was reached, and a plan committed to the repo can be diffed against what was actually built, audited later, and read by any agent you adopt next without a migration.
How do I stop maintaining separate config files per agent?: Move project knowledge out of tool configuration and into a searchable index. RDK stores your code, docs, and decisions as encrypted private chunks any agent can query through a retrieval tool. You record a convention once and every agent gets it, instead of three config files drifting apart at three different rates.
Does running multiple agents cost more in tokens?: It does if each one relearns your project every session, which is the usual setup. With a shared retrieval layer the agents pull the relevant chunk instead of rediscovering your architecture. Stacked retrieval keeps 40 to 65 percent of queries in your private index and another 15 to 20 percent in the public network, so token spend drops 80 to 90 percent.

Key takeaways

Different agents fail differently. That is the entire argument for using more than one, and it only pays off if you exploit the difference deliberately.
Split by stage, not by task. Planning, implementation, and review are separable, and the handoff between them is a file rather than a conversation.
Cross-model review beats self-review. An agent asked to check its own work tends to defend it; a different model with no attachment to the code finds more.
The hidden tax is configuration drift. Every agent wants its own config file, and three copies of your conventions go stale at three different rates.
Keep one knowledge layer outside every tool. Any agent that can call a retrieval tool reads the same source of truth, so what you teach one, you have taught all of them.