version: "1.0.0" 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.

1. Implement aggregation for parallel tasks: merge strategy + conflict rules, tested on fixture

data.

1. Load-test: saturate the system, watch backpressure and degradation — graceful slowdown, not

collapse.

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.