Introduction to Task Decomposition and Orchestration

What is Orchestration?

Imagine directing a symphony. Each musician plays their instrument expertly, but someone must coordinate when each section enters, how loud they play, and how the pieces fit together. In multi-agent systems, an orchestrator performs this same role—coordinating specialized agents to complete complex tasks that no single agent could handle alone.

An orchestrator doesn't do the work itself. Instead, it breaks down complex tasks into manageable pieces, assigns those pieces to specialized agents, monitors their progress, and combines their results into a coherent final output. This separation between coordination logic and execution logic is what makes large-scale AI systems manageable.

Why Decompose Tasks?

Breaking complex tasks into smaller subtasks through orchestration provides three critical benefits. Parallelization enables faster completion by running independent subtasks simultaneously—instead of waiting for a research agent to finish before starting writing, both can work in parallel when their tasks don't depend on each other. Specialization improves output quality because focused agents excel at specific tasks—a research agent trained on academic literature produces better summaries than a general-purpose agent, while a code generation agent writes cleaner code than a research agent. Maintainability keeps codebases manageable because each agent has a clear, bounded responsibility—debugging a failing research step is simpler when the research logic lives in a dedicated agent rather than scattered across a monolithic system.

Three Orchestration Patterns

Modern orchestration systems rely on three fundamental patterns, each suited to different task structures.

Sequential orchestration organizes tasks in a pipeline where each step depends on the previous step's output. Task A completes and passes its result to Task B, which processes it and passes to Task C. This pattern appears in research workflows where literature search must complete before summarization, which must complete before synthesis. The orchestrator manages the handoffs between stages and ensures each stage receives the correct input from its predecessor.

Parallel orchestration runs independent tasks simultaneously and combines their results. Tasks A, B, and C execute at the same time, and the orchestrator waits for all to complete before aggregating outputs. This map-reduce pattern accelerates workflows where subtasks don't depend on each other—analyzing ten research papers can happen in parallel, with results merged afterward. The orchestrator handles task distribution, monitors completion status, and implements the aggregation logic.

Hierarchical orchestration creates multi-level coordination through nested orchestrators. A top-level orchestrator delegates to sub-orchestrators, which manage specialized worker agents. This tree structure scales to complex projects where a main orchestrator coordinates research, writing, and review phases, each managed by a phase-specific orchestrator controlling multiple specialist agents. The hierarchy allows both high-level project coordination and fine-grained task control without overwhelming complexity.

What This Course Teaches

This course builds practical multi-agent orchestration systems from the ground up. Students will understand how orchestration patterns solve real coordination challenges in AI systems, learning when sequential pipelines outperform parallel execution and how hierarchical structures manage complexity. The course demonstrates building systems with four or more coordinated agents, showing how to design agent interfaces, implement communication protocols, and handle failure cases when agents don't respond as expected.

The curriculum emphasizes implementing coordination strategies that balance agent autonomy with system reliability. Students learn to design orchestrators that monitor agent health, retry failed subtasks, and gracefully degrade when agents are unavailable. By the end, participants will confidently architect multi-agent systems that decompose complex tasks into coordinated subtask execution.

Course Deliverables

Prerequisites

Students should be comfortable with asynchronous programming concepts, as orchestrators manage multiple concurrent agent interactions. Familiarity with state machines or workflow engines is helpful but not required—the course teaches orchestration-specific state management from first principles.

Next Steps

The following chapter covers rapid prototyping techniques to build a working orchestrator in 15 minutes, demonstrating the core coordination loop before diving into advanced patterns. This hands-on start ensures immediate practical experience with orchestration concepts.