Agentic Systems: From Single Agent to Orchestration A developer explains that multi-agent orchestration becomes necessary when tasks decompose into sub-tasks requiring different contexts, tools, or roles. The post covers production patterns like hierarchical, sequential, and peer-to-peer orchestration, and provides a Rust code example of a coordinator delegating to worker agents. Key failure modes of single-agent systems include context window overload, tool conflicts, and lack of specialization. A single agent — one model, a system prompt, a handful of tools, and a loop — gets you surprisingly far. It's also where most people stop, because it works for demos and falls over the moment a task needs more than one kind of reasoning happening at once. Multi-agent orchestration isn't a hype upgrade over a single agent — it's what you reach for when a task naturally decomposes into sub-tasks that benefit from different context, different tools, or genuinely different roles. This post covers when that shift is warranted, the orchestration patterns actually used in production, and a small working example of a coordinator delegating to worker agents. A single agent's context window and tool list grow with every capability you bolt on. Three failure modes show up as that happens: Orchestration solves this by splitting one large, blurry job into a coordinator plus specialized workers, each with a narrow role, its own context, and only the tools it actually needs. There isn't one "multi-agent architecture" — a few concrete patterns cover most real systems: Frameworks like LangGraph model this as an explicit state graph, CrewAI models it as role-based "crews," and minimal SDKs like the OpenAI Agents SDK treat sub-agents as callable tools from a parent agent. The underlying pattern is the same regardless of framework — what differs is how much structure and observability you get by default. Here's a minimal hierarchical setup: a coordinator agent receives a research request, decomposes it into sub-tasks, dispatches them to worker agents concurrently, and merges the results. use futures::future::join all; use serde::{Deserialize, Serialize}; derive Debug, Clone, Serialize, Deserialize struct SubTask { id: String, description: String, assigned role: AgentRole, } derive Debug, Clone, Serialize, Deserialize enum AgentRole { Researcher, CodeAnalyst, Summarizer, } derive Debug, Serialize struct WorkerResult { task id: String, output: String, tokens used: u32, } struct Coordinator { llm client: LlmClient, } impl Coordinator { /// Decompose a high-level request into sub-tasks using the coordinator's own LLM call. async fn plan &self, request: &str - Vec