Why Task Orchestration Matters
A single AI agent running a single task is simple. But real-world applications involve dozens of tasks, multiple agents, dependencies between tasks, and the need for reliability at every step. This is where orchestration becomes essential.
Task orchestration is the difference between a collection of AI agents and an AI-powered system.
Core Orchestration Concepts
Task Routing
Not every agent is suited for every task. Routing ensures the right task goes to the right agent based on:
- Agent capabilities: Which skills does the agent have?
- Current load: Is the agent already busy?
- Cost optimization: Can a cheaper agent handle this task?
- Priority: Should this task preempt current work?
Dependency Management
Tasks often depend on other tasks. Task B can't start until Task A completes. A good orchestration system manages these dependencies automatically:
- Sequential dependencies: A → B → C
- Parallel groups: (A, B, C) all run simultaneously, D waits for all three
- Conditional branches: If A succeeds, run B; if A fails, run C
Load Balancing
When you have multiple agents capable of handling the same task type, load balancing distributes work evenly to maximize throughput and minimize latency.
Queue Management
Tasks that can't be executed immediately go into queues. Effective queue management includes priority ordering, timeout handling, and dead-letter queues for failed tasks.
Orchestration Patterns in Practice
Pattern 1: Simple Queue
Tasks enter a queue and are picked up by the next available agent. Great for homogeneous workloads where any agent can handle any task.
Pattern 2: Skill-Based Routing
Tasks are tagged with required skills and routed only to agents that have those skills. This is the default pattern in ClawHQ — when you create a task, you specify required skills, and the orchestrator routes it to a capable agent.
Pattern 3: Pipeline
Tasks flow through a series of stages, each handled by a different agent or agent pool. Output from one stage becomes input for the next. See our guide on multi-agent workflows for a hands-on example.
Pattern 4: Map-Reduce
A large task is split into subtasks (map), distributed across agents, and results are aggregated (reduce). Ideal for data processing, analysis, and batch operations.
Pattern 5: Saga
For long-running processes with multiple steps that need to be reversed if something fails. Each step has a corresponding compensation action. Useful for business processes with side effects.
Orchestration in ClawHQ
ClawHQ provides a visual orchestration layer that lets you:
- Design workflows: Create task flows visually or with code
- Configure routing rules: Set up skill-based, cost-optimized, or priority-based routing
- Monitor execution: Watch tasks flow through your pipeline in real time
- Handle failures: Configure retry, fallback, and escalation policies
- Track performance: See throughput, latency, and bottlenecks per stage
Building Resilient Orchestration
Retry Strategies
Not all failures are permanent. Network timeouts, rate limits, and transient errors often resolve with a retry. Configure:
- Immediate retry: For transient errors
- Exponential backoff: For rate limits
- Max retries: To prevent infinite loops
Circuit Breakers
If an agent or external service is consistently failing, stop sending it work. A circuit breaker tracks failure rates and "opens" (stops routing) when the rate exceeds a threshold. It periodically tests with a single request and "closes" (resumes routing) when the service recovers.
Dead Letter Queues
Tasks that fail after all retries go to a dead letter queue for manual review. Don't just drop failed tasks — they often contain valuable information about system issues.
Scaling Orchestration
As your fleet grows, orchestration complexity grows with it. Key strategies for scaling:
- Horizontal scaling: Add more agents to bottleneck stages
- Queue sharding: Split large queues by task type or priority
- Caching: Cache common results to avoid redundant work
- Async processing: Not everything needs to be real-time
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