Advanced

Multi-Agent Orchestration

How to run AI agent teams that work 24/7 — coordinating multiple agents on complex tasks with predictable quality and cost control.

What Is Agent Orchestration?

Agent orchestration is the practice of coordinating multiple AI agents to complete a task that no single agent could handle alone. Instead of one agent doing everything sequentially, you decompose the work and assign pieces to specialists working in parallel.

A well-orchestrated team of 5 agents can finish in 2 minutes what a single agent takes 10 minutes to do — with higher quality, because each agent focuses on what it does best: one writes code, another reviews it, a third writes tests, and a fourth updates documentation.

Team Topologies

Star (Hub-and-Spoke)

A single orchestrator assigns tasks and collects results from specialist agents. Simple to reason about and debug.

Best for: Small teams (3-5 agents), sequential pipelines, clear ownership per subtask.

Mesh (Peer-to-Peer)

Every agent can communicate with every other agent via shared files or message passing. No single coordinator.

Best for: Creative brainstorming, consensus tasks, redundancy-critical workflows.

Hierarchical (Tree)

Agents form a tree structure. A Queen agent delegates to team leads, who delegate to workers. Mirrors org charts.

Best for: Large projects (10+ agents), multi-domain tasks, production engineering workflows.

Pipeline (Assembly Line)

Each agent handles one stage and passes output to the next. Stage N+1 starts only after stage N completes.

Best for: ETL workflows, content pipelines (research then write then edit then publish).

The Orchestration Loop

Inspired by production engineering, every orchestrated workflow follows a four-phase loop. The loop repeats until all outputs pass verification.

Plan

The orchestrator decomposes the goal into discrete tasks, assigns each to an agent, and sets acceptance criteria.

Execute

Agents work in parallel (or sequence) on their assigned tasks, writing structured output to shared files.

Verify

A validator agent checks each output against the acceptance criteria. Outputs are scored 1-10.

Fix

Any output scoring below 7 is sent back to the original agent with specific feedback for revision.

Task Decomposition

The orchestrator breaks large goals into agent-sized pieces. A good subtask is self-contained, has clear inputs and outputs, and can be verified independently.

Good decomposition

- Each subtask has a single responsibility
- Inputs and outputs are structured (JSON, markdown)
- Subtasks can run in parallel where possible
- Acceptance criteria are defined upfront

Bad decomposition

- Subtasks depend on shared mutable state
- One agent needs another agent's half-finished work
- No clear way to verify if a subtask succeeded
- Tasks are too large (entire features) or too small (single lines)

Swarm Patterns

A swarm is a specific implementation of orchestration where agents coordinate through shared state files and consensus protocols.

# Queen/Worker Topology
Queen Agent
  ├── Assigns tasks from the backlog
  ├── Monitors worker status via shared coordination file
  └── Aggregates results when all workers report "done"

Worker Agents (up to 8-10)
  ├── Pull tasks from the shared queue
  ├── Write structured output to their result file
  └── Signal completion via status update

Consensus Coordination (Raft protocol)
  ├── Workers vote on ambiguous decisions
  ├── Majority wins — ties go to the Queen
  └── Dissenting opinions are logged for review

Cost Tracking

Multi-agent workflows consume tokens fast. Track costs per session to avoid surprises and optimize your agent team composition over time.

What to monitor

Token usage per agent: Track input and output tokens separately. Review agents use fewer output tokens than coding agents.
Tool calls per session: Each tool call adds latency and cost. Agents making 50+ tool calls may need better prompts.
Estimated cost per task: Log the total cost of each orchestrated task. Compare single-agent vs. multi-agent cost for the same work.
Retry rate: Tasks that fail verification and get retried double the cost. High retry rates signal poor task decomposition.

Quality Gates

Every agent output is scored 1-10 before it ships. High-scoring patterns get promoted to the knowledge base automatically. Low scores trigger immediate fixes.

Score	Label	Action
9-10	Excellent	Promote the pattern to the knowledge base for reuse across future sessions.
7-8	Good	Ship as-is. No corrective action needed.
5-6	Mediocre	Document gaps. Create a follow-up task. Do not ship without revision.
1-4	Failed	Fix immediately. Spawn a review sub-agent. Root-cause the failure.

Observability

You cannot improve what you cannot measure. Every orchestrated session should produce a structured log that answers: what happened, how long it took, and what it cost.

// learning.json — written after every session
{
  "session_id": "2026-04-01-feature-auth",
  "agents_spawned": 5,
  "tasks_completed": 12,
  "tasks_retried": 2,
  "avg_quality_score": 8.3,
  "total_tokens": 145200,
  "estimated_cost_usd": 0.42,
  "patterns_promoted": ["auth-middleware-pattern"],
  "duration_minutes": 6.2
}

Scaling from 3 to 30 Agents

Capability Registry

Maintain a manifest of what each agent can do. The orchestrator consults this registry before assigning tasks, avoiding mismatched assignments.

Knowledge Sharing

Use a shared knowledge base (markdown or JSON) that any agent can read and write. New learnings propagate to the entire team automatically.

Backpressure

Limit concurrent agents to 8-10. Beyond that, context switching and coordination overhead outweigh the parallelism gains.

Specialization over Generalization

A 30-agent team should have narrow specialists (security reviewer, test writer, docs updater), not 30 generalists doing everything.

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