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Multi-Agent Orchestration

Once individual workflows are trustworthy, the next leverage is running them together: parallel agents that implement, test, and document a change as a single coordinated unit of work, rather than one agent doing everything sequentially.

A single agent doing implementation, then tests, then docs is a queue. Three agents doing them concurrently — sharing the same plan and context — is a team. The platform's job is to be the coordination layer that keeps that team consistent.

This is where the orchestration patterns and shared memory earn their keep. The risk is also highest here: more agents means more ways to drift, conflict, and sprawl.

Structure​

An orchestrator decomposes the task and shares one plan; specialized agents work in parallel against shared memory; the orchestrator reconciles their output into one coherent change.

How It Works​

  1. Decompose — the orchestrator breaks the task into independent sub-tasks (implement, test, document) and writes a shared plan.
  2. Fan out — specialized agents pick up sub-tasks and run concurrently, each reading from and writing to shared memory.
  3. Coordinate — agents stay consistent by referencing the same plan and the same context layer, not by guessing at each other's work.
  4. Reconcile — the orchestrator merges outputs, resolves conflicts (the test agent found a bug → loop back to implementation), and produces one reviewable artifact.

Key Characteristics​

  • Parallelism cuts wall-clock time — independent sub-tasks run at once instead of in a queue. This is the headline benefit.
  • Shared context prevents drift — agents that don't share a plan produce a PR where the code, tests, and docs describe three different features. Shared memory is the consistency mechanism.
  • The orchestrator is a single point of failure — and a single point of cost. Every request pays for its reasoning plus the workers'. Budget accordingly.
  • Reconciliation is the hard part — fan-out is easy; merging conflicting outputs into something coherent is where the engineering is.
  • More agents, more surface area — each added agent multiplies the ways the system can fail. Add them only when the parallelism clearly pays for the coordination cost.

When to Use​

  • A unit of work has genuinely independent sub-tasks that can run concurrently.
  • The sub-tasks share enough context that coordinating them beats running them separately.
  • Wall-clock latency matters and the sub-tasks are individually trustworthy already.

Pitfalls​

  • Agent Sprawl — spinning up a dozen agents because you can, not because the work decomposes that way. Start with the smallest set that maps to real, independent sub-tasks.
  • The Framework Trap — reaching for a heavyweight multi-agent framework before you've proven you need multi-agent. Most "multi-agent" problems are one good agent away from solved.
  • Unattributable output — when three agents touch a PR, you still need to know which one did what. Preserve attribution through the reconcile step.