Agent Orchestration

---
name: agent-orchestration
description: Orchestrate agents at runtime — task routing, scheduling, parallel execution, result aggregation, and supervision dashboards. Use when running agents as a managed fleet rather than one-off calls.
category: ai-research
---

# Agent Orchestration

Orchestration is the runtime layer: taking agents as workers and running them as a system — 
routing tasks, scheduling execution, handling parallelism, aggregating results, and supervising the 
whole fleet. Architecture designs the team; orchestration runs it.

## Overview

An orchestrator owns the task lifecycle: intake (validate and normalize requests), planning 
(decompose into subtasks), dispatch (assign to agents with briefs), supervision (track progress, 
handle stalls), aggregation (combine results), and delivery. Underneath: queues, worker pools, 
retries, timeouts, and rate limits. The orchestrator is also the policy enforcement point — 
budgets, permissions, and escalation all live here.

## When to use

- Running many agent tasks concurrently: batch jobs, user-facing agent fleets, background 
processing.
- Workflows where tasks have dependencies and need scheduling, not just parallel blasting.
- Production agent systems needing reliability: retries, failover, backpressure.
- When you need visibility: what's running, what's stuck, what's costing money.

## Core concepts

- **Task model**: tasks as structured objects — id, type, payload, priority, deadline, retry 
policy. Everything the orchestrator touches is a task.
- **Routing**: matching tasks to agents by capability, load, and cost. Simple round-robin to start; 
capability-aware routing as the fleet diversifies.
- **Scheduling**: ordering with dependencies (DAGs), priorities, and deadlines. Not everything runs 
now; the scheduler decides what runs when.
- **Worker pools**: agents as managed workers with concurrency limits, health checks, and warm/cold 
lifecycle. Protects against overload.
- **Supervision**: heartbeats, progress reports, stall detection, and intervention — pause, 
reassign, escalate. The orchestrator never fire-and-forgets.
- **Aggregation**: combining parallel results — merge, vote, synthesize — with conflict 
resolution rules defined up front.

## Practical workflow

1. Define the task schema and the agent capability registry (what each agent/worker type can do).
2. Build intake: validate tasks, assign priorities and deadlines, enqueue durably.
3. Implement dispatch with concurrency limits per worker type; enforce timeouts and retry policies.
4. Add supervision: heartbeat monitoring, stall detection, and an operator dashboard showing task 
states.
5. Implement aggregation for parallel tasks: merge strategy + conflict rules, tested on fixture 
data.
6. Load-test: saturate the system, watch backpressure and degradation — graceful slowdown, not 
collapse.

```text
Orchestrator components:
INTAKE → validate, prioritize, enqueue (durable queue)
SCHEDULER → DAG + priority + deadline ordering
DISPATCH → capability-matched assignment, concurrency caps
SUPERVISE → heartbeats, stall detection, intervention
AGGREGATE → merge/vote/synthesize with conflict rules
POLICY → budgets, permissions, escalation (enforced here)
```

## Common pitfalls

- **No backpressure**: accepting unlimited tasks until the system melts. Queue, shed load, or 
degrade gracefully — pick one.
- **Fire-and-forget dispatch**: tasks sent with no tracking. Every dispatch gets a tracked 
lifecycle.
- **Retry storms**: blind retries amplifying a downstream failure. Exponential backoff, circuit 
breakers, dead-letter queues.
- **Invisibility**: no dashboard, no alerts. You find out about failures from users. Instrument 
from day one.
- **Policy in agents**: budgets and permissions implemented per-agent instead of at the 
orchestrator. Centralize enforcement.
- **Aggregation as an afterthought**: parallel results that don't combine cleanly. Design the merge 
before parallelizing.