Why the Orchestration Pattern Matters for Business
An architect of a multi-agent system faces a choice similar to that of a manager building a team — is it better to hire one versatile person or a group of specialists with a coordinator? In the world of AI agents, this decision directly translates into result quality, task completion time, operational costs, and scalability. There is no single universal pattern — each has its strengths and limitations.
Sequential Pattern (Pipeline)
The simplest and most predictable pattern. Agent A processes the input and passes the result to Agent B, who passes it to Agent C. Each pipeline stage performs one well-defined task. The sequential pattern works well where the step order is deterministic and each stage depends on the previous result — for example in a process of: document retrieval, data extraction, validation, system save.
The drawback is low fault tolerance — a single agent failure blocks the entire pipeline — and lack of parallelism, which extends processing time for large document volumes. In practice, a sequential pipeline should be supplemented with retry mechanisms and buffer queues between stages.
Parallel Pattern (Fan-Out / Fan-In)
When a task can be divided into independent subtasks, the parallel pattern dramatically reduces completion time. An orchestrator agent distributes the task among N execution agents (fan-out), collects results, and synthesizes the final response (fan-in). A classic example: counterparty risk analysis requiring simultaneous checks of registry data, payment history, press coverage, and social media signals.
- Fan-out/fan-in — splitting into independent subtasks with result aggregation
- Concurrent multi-source verification — the same document analyzed by specialists from different domains
- Redundancy with voting — multiple agents solve the same problem, the result is determined by majority vote
Hierarchical Pattern
In complex organizations, delegating responsibility down the hierarchy is natural. Similarly, in multi-agent systems, a manager agent accepts a task from the user, decomposes it into subtasks, and delegates them to specialized subordinate agents. Subordinate agents can in turn have their own sub-teams. This pattern excellently models real business processes where different departments execute their parts of a project under coordinator oversight.
The key advantage of hierarchy is natural scope management and the ability to replace individual agents without redesigning the entire system. The challenge is latency — each hierarchy level adds wait time — and the risk of cascading errors when a higher-level agent misinterprets a situation.
Agent Network with Shared Memory
The most advanced pattern mimics the functioning of a human team with a shared workspace. Agents communicate not through rigid connections but through shared context — a knowledge base, task board, decision history. Each agent observes the shared state and reacts to events relevant to its specialization. The system is resilient to individual agent failures and can autonomously reorganize work.
ESKOM.AI uses a combination of these patterns depending on the nature of the automated process. For tasks with well-defined workflows — pipeline. For multi-dimensional analysis — parallelism. For complex projects requiring coordination — hierarchy with manager agents. Choosing the right pattern is the foundation of a system that scales with the organization.