Not every problem needs a swarm of agents. And not every simple problem should be solved by a single agent either. The orchestration pattern you choose has profound implications for debuggability, cost, latency, and reliability.
Let’s break down the patterns and when each makes sense.
Single agent pattern
Structure: One agent with access to multiple tools, handling the complete task.
Best for:
- Well-defined tasks with clear boundaries
- Situations where context needs to flow through the entire process
- Cost-sensitive applications where token efficiency matters
Watch out for:
- Context window limitations on complex tasks
- Tool sprawl making the agent’s decision space too large
- Single point of failure
The single agent pattern is underrated. Many teams jump to multi-agent architectures prematurely, adding complexity without proportional benefit.
Sequential pipeline pattern
Structure: Multiple specialized agents, each handling one phase, passing results forward.
Best for:
- Tasks with clear phases (research → analysis → synthesis)
- Situations where different expertise is needed at each stage
- Workflows where intermediate outputs are valuable artifacts
Watch out for:
- Error propagation (garbage in at stage 1 means garbage out at stage 4)
- Latency accumulation across stages
- Difficulty with tasks that require iteration
Sequential pipelines work well when you can cleanly decompose a task and when the output of each stage is well-defined.
Parallel execution pattern
Structure: Multiple agents working simultaneously on independent subtasks, results aggregated.
Best for:
- Tasks that decompose into independent chunks
- Situations where latency matters more than cost
- Research and analysis across multiple domains
Watch out for:
- Coordination overhead
- Result aggregation complexity
- Inconsistent outputs requiring reconciliation
Parallelization shines when subtasks are truly independent. If agents need to coordinate mid-task, you’re often better with a different pattern.
Hierarchical orchestration
Structure: A coordinator agent that delegates to specialist agents, managing the overall workflow.
Best for:
- Complex, multi-faceted problems
- Situations requiring dynamic task decomposition
- Workflows where the path depends on intermediate results
Watch out for:
- Coordinator becoming a bottleneck
- Increased debugging complexity
- Higher overall cost
This is the most flexible pattern, but flexibility comes with operational complexity.
Making the choice
Start with the simplest pattern that could work. Single agent should be your default until you have evidence it’s insufficient.
Ask these questions:
- Can a single agent hold enough context to complete the task?
- Are there natural phase boundaries in the work?
- Are subtasks truly independent?
- Do you need dynamic routing based on intermediate results?
The answers will point you toward the right pattern. And remember—you can always evolve your architecture. Starting simple and adding complexity is far easier than untangling an over-engineered system.