AI Agent Orchestration: Patterns for Coordinating Complex Agents
Most interesting tasks can’t be solved in a single prompt. They require steps, dependencies, and decisions made along the way. Agent orchestration is the coordination layer that handles all of that: what runs first, what runs in parallel, who decides what to do next, and how results get assembled into something useful.
Orchestration is not a framework. It is a set of patterns. You can implement them with raw API calls, a Python script, or any agent framework you prefer. The pattern is what matters. The implementation is a detail.
The Three Core Patterns
Sequential Chains
The most basic pattern: step one produces output, step two consumes it, step three consumes step two’s output. The chain runs in order, and no step starts until the previous one finishes.
This pattern fits tasks with hard dependencies. You can’t summarize a document you haven’t fetched. You can’t write a product description without knowing the specs. Wherever step N genuinely requires the output of step N-1, a sequential chain is the right structure.
The cost is time. Each step adds latency, and in a sequential chain you pay that cost in full. If some middle steps don’t actually depend on each other, that is a hint to look at the next pattern.
Parallel Fan-Out
When subtasks are independent, run them at the same time. A parallel fan-out dispatches multiple tasks concurrently, waits for all of them to complete, and then aggregates the results.
The canonical use case is research. Say an agent needs to compare five competing products. It can fire off five simultaneous searches, get five results back, and feed them all to a final synthesis step. The total time is roughly equal to the slowest single search, not the sum of all five.
Fan-outs work whenever the subtasks don’t share state and don’t depend on each other’s outputs. Gathering data from multiple APIs, checking several sources for the same fact, running the same analysis against multiple inputs: all of these fit. The aggregation step at the end is usually a model call that takes all the parallel outputs and turns them into a coherent result.
Supervisor-Worker
The most flexible pattern, and the most complex. A supervisor agent receives the top-level goal, breaks it down into subtasks, dispatches each subtask to a worker agent or tool, collects the outputs, and decides what to do next. The supervisor may dispatch additional subtasks based on what it learns. When it has enough information, it synthesizes a final response.
This pattern fits tasks that benefit from specialization. A legal analysis task might dispatch to a case-law search worker, a statute-lookup worker, and a document-summary worker. Each one has access to tools matched to its job. The supervisor doesn’t need to know how those tools work. It knows that the workers can handle those categories of subtask.
Supervisor-worker also handles tasks where the plan isn’t fully known upfront. The supervisor can look at intermediate results and adjust: if initial research turns up a complication, it dispatches a follow-up task before moving to synthesis. Plan a bit, do a bit, update the plan.
What Orchestration Actually Enables
These patterns open up tasks that a single prompt can’t touch.
A single-pass prompt has a fixed context window. You can stuff a lot into it, but it doesn’t scale to “analyze 50 competitor websites and produce a detailed report.” You need to break that into steps, manage what goes where, and combine results at the end. Orchestration is what makes that possible.
Tool calls fit naturally into each pattern. In a sequential chain, each step might call a different tool: fetch a document, extract key facts, search for related information, write a summary. In a parallel fan-out, each branch might call the same tool with different inputs. In a supervisor-worker system, each worker has its own tool set. The orchestration layer determines when each call happens and what gets done with the result.
This is also where the tool connection question comes up. An orchestrated system can have a dozen agents in flight at once, each needing access to search, news, financial data, or document retrieval. Managing separate API credentials for each tool, across each worker, adds up fast.
Where AgentPatch Fits In
AgentPatch is a marketplace of tools for AI agents, exposed through a single MCP connection. Instead of wiring up separate API keys for search, news, and financial data, each agent in your pipeline connects to AgentPatch once and calls whatever tool it needs.
For orchestrated systems, this matters. In a parallel fan-out with six research workers, each worker needs search access. With AgentPatch, that’s one connection per worker, no per-service setup. In a supervisor-worker system, specialist agents call different tools: google-search and scrape-web for web research, google-news for the news worker, sec-company-financials for the data worker. All through the same endpoint.
The catalog covers web search, news, academic papers, maps, financial data, image generation, email, and more. One account, one bill. Failed tool calls are refunded.
Wrapping Up
Sequential chains for dependent steps. Parallel fan-outs for independent research. Supervisor-worker for complex tasks that need specialization and adaptive planning. These three patterns cover most of what production agent systems actually do.
If you’re building something that needs real-world data, search, or document access across those patterns, take a look at agentpatch.ai. One MCP connection, 40-plus tools, no per-service credentials to manage.