How to Orchestrate Multiple AI Coding Agents

What orchestrating multiple agents actually means

A single agent runs one loop: read a prompt, call tools, reason, respond. Orchestration is what you add when one loop is not enough, when a task is too large, too parallel, or too specialized for one agent to handle well. You split the work across several agents and coordinate them.

That coordination is the real problem, and it is not about which model you use. Tools like cmux, Claude Code subagents, or a Pi-style agent runner are just harnesses for spawning and supervising agents. Once you have more than one agent running, three questions decide whether the system works: how do you divide the task, how does each agent get the context it needs, and how do you merge the results back together without contradictions.

Get those three right and orchestration feels like magic. Get them wrong and you get a pile of agents that duplicate each other's work, disagree about the state of the codebase, and cost more than the single agent you were trying to speed up.

The three core orchestration patterns

Almost every multi-agent setup is built from three primitives. Learn them separately, then combine them.

The planner/worker pattern uses one lead agent to break a goal into subtasks and delegate each to a worker agent. The planner holds the big picture; the workers stay narrow and focused. This is how Claude Code subagents and cmux-style orchestrators handle a feature request: a lead decomposes it, workers implement the pieces, the lead reviews and integrates.

Parallel fan-out runs several agents at once on independent subtasks, then collects their outputs. It is the fastest pattern when subtasks do not depend on each other, like writing tests for ten separate modules. The catch is that fan-out multiplies cost and multiplies the chance of two agents making incompatible assumptions.

Sequential hand-off chains agents in a pipeline, where each one's output is the next one's input: a research agent gathers context, a coding agent implements, a review agent checks. Hand-offs are clean to reason about but fragile, because everything the downstream agent needs has to survive the hand-off.

Where multi-agent orchestration fails

The failure modes are predictable, which means you can design against them.

Context duplication is the most common and the most expensive. Every agent needs to understand the codebase, so every agent re-reads the same files and re-asks the LLM the same background questions. Ten workers means the same project context is loaded and paid for ten times.

Cost explosion at fan-out follows directly. Your bill scales with the number of agents multiplied by the context each one carries. Doubling your workers to go faster can quadruple spend once you count the redundant context each new worker drags into its prompt.

Conflicting writes happen when parallel agents edit the same files or make contradictory design decisions, because none of them can see what the others are doing. And lost hand-off context is the pipeline killer: the coding agent never learns the constraint the research agent discovered, because it was never written anywhere both agents could read. Notice that three of these four failures are really one problem, agents that cannot share what they know.

Shared memory: giving agents a common source of truth

The fix for duplication, cost, and lost hand-offs is a shared memory layer that every agent reads from and writes to. Instead of each agent independently rediscovering the project by re-reading files and re-querying the model, the knowledge lives once in a searchable store, and agents retrieve the slice they need on demand.

This changes the economics of fan-out. When a worker needs to know how authentication is structured, it retrieves the relevant chunks from the shared index rather than reading the whole auth module and asking the LLM to summarize it. The first agent to need that context pays to establish it; every other agent recalls it cheaply. The context is created once and reused across the whole fleet.

A shared retrieval layer also solves hand-offs. When the research agent writes its findings into the shared store, the coding agent downstream retrieves them instead of depending on a fragile prompt-to-prompt pass. Common memory turns a chain of hand-offs into a set of agents reading from the same page.

Where RDK fits: a shared retrieval layer for the whole fleet

RDK is the shared retrieval layer you point every agent at. You index the codebase, docs, and prior decisions once as encrypted chunks, and each agent, whether it runs under cmux, Claude Code, or a Pi-style runner, queries that index before it queries an LLM. Retrieval is common memory across the fleet.

This is what controls cost at fan-out. The planner retrieves to decide how to split the work. Each parallel worker retrieves the exact chunks its subtask needs rather than re-loading the repository. Because a retrieved chunk usually already contains the answer, the agent quotes it instead of paying the LLM to regenerate reasoning another agent already did. Recall is cheaper than regeneration, and shared recall means the fleet pays for a piece of context once, not once per agent.

RDK calls the strategy stacked retrieval. Private chunks from your own indexed project answer the bulk of queries, a public network of published chunks catches more, and the LLM handles only the genuinely novel questions no stored knowledge covers. Applied across a multi-agent system, this keeps token spend roughly flat as you add workers, because the extra agents are recalling shared knowledge rather than each buying it fresh. The orchestration pattern decides whether your agents are correct; the shared retrieval layer decides whether adding more of them stays affordable.