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How to run AI agent teams that work 24/7 — coordinating multiple agents on complex tasks with predictable quality and cost control.
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.
A single orchestrator assigns tasks and collects results from specialist agents. Simple to reason about and debug.
Every agent can communicate with every other agent via shared files or message passing. No single coordinator.
Agents form a tree structure. A Queen agent delegates to team leads, who delegate to workers. Mirrors org charts.
Each agent handles one stage and passes output to the next. Stage N+1 starts only after stage N completes.
Inspired by production engineering, every orchestrated workflow follows a four-phase loop. The loop repeats until all outputs pass verification.
The orchestrator decomposes the goal into discrete tasks, assigns each to an agent, and sets acceptance criteria.
Agents work in parallel (or sequence) on their assigned tasks, writing structured output to shared files.
A validator agent checks each output against the acceptance criteria. Outputs are scored 1-10.
Any output scoring below 7 is sent back to the original agent with specific feedback for revision.
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.
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
Multi-agent workflows consume tokens fast. Track costs per session to avoid surprises and optimize your agent team composition over time.
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. |
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
}Maintain a manifest of what each agent can do. The orchestrator consults this registry before assigning tasks, avoiding mismatched assignments.
Use a shared knowledge base (markdown or JSON) that any agent can read and write. New learnings propagate to the entire team automatically.
Limit concurrent agents to 8-10. Beyond that, context switching and coordination overhead outweigh the parallelism gains.
A 30-agent team should have narrow specialists (security reviewer, test writer, docs updater), not 30 generalists doing everything.
AI Brain Pro ($97) ships with multi-agent orchestration, swarm coordination, quality gates, observability templates, and 130 agents pre-configured. One-time purchase. No subscription.
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