Multiple agents working together can accomplish more than a single agent working alone. The question is how to organize that collaboration. After watching many multi-agent systems succeed or fail, a clear pattern emerges: hierarchical delegation - where an orchestrator coordinates specialized sub-agents - works reliably, while flat peer collaboration often devolves into confusion. Understanding why hierarchy succeeds helps you build multi-agent systems that actually deliver on the collaboration promise.
The Collaboration Challenge
The appeal of multi-agent systems is easy to understand. Complex tasks often involve multiple distinct skills. Research requires finding information, analysis requires processing it, writing requires synthesizing it. A single agent attempting all of these might be mediocre at each. Specialized agents can excel at their particular skill.
The challenge is coordination. Multiple agents need some way to divide work, share results, avoid stepping on each other, and combine their outputs into coherent deliverables. Without good coordination, you get chaos - agents duplicating effort, contradicting each other, or working at cross purposes.
Several coordination models exist. Flat collaboration has all agents as peers, sharing a common workspace and deciding among themselves who does what. Round-robin routing sends tasks to agents in sequence, with each handing off to the next. Hierarchical delegation has one orchestrator that assigns tasks to specialists and integrates their results.
In theory, flat collaboration most closely mimics how human teams sometimes work - everyone contributes, decisions emerge through discussion. In practice, flat collaboration fails for agents because agents lack the implicit coordination mechanisms humans rely on. They cannot read the room, notice when they are duplicating effort, or defer to expertise. They need explicit structure.
Why Hierarchy Works
Hierarchical delegation succeeds because it provides explicit structure that agents can follow reliably.
The orchestrator holds the overall task understanding and decides how to break it into pieces. It knows what needs to happen, in what order, and how results from different specialists should combine. This single point of coordination eliminates the confusion that arises when multiple agents try to coordinate themselves.
The specialists focus on their particular capability without worrying about the broader context. A research specialist finds information. An analysis specialist processes data. Each does one thing well. The narrow focus allows for better prompts, more appropriate tools, and more reliable behavior.
Results flow upward from specialists to orchestrator. The orchestrator receives structured outputs and decides how to use them - combining results, routing to the next specialist, or producing final output. This unidirectional flow is simple to reason about and debug.
Delegation flows downward from orchestrator to specialists. The orchestrator provides enough context for each task without overwhelming specialists with irrelevant information. Each specialist sees only what it needs for its piece of the work.
This structure maps well to how effective human organizations handle complex work. A project manager coordinates specialists who each contribute their expertise. The project manager does not need to be the best researcher, analyst, and writer. They need to understand what each specialist contributes and how the pieces fit together.
Designing an Orchestrator
The orchestrator is the critical piece of a hierarchical system. Its job is understanding the overall task, breaking it into appropriate sub-tasks, delegating to the right specialists, and integrating results.
A good orchestrator prompt establishes what specialists are available and what each does well. It explains how to break down tasks and what information to provide when delegating. It describes how to combine results into coherent outputs. It includes guidance on handling failures - what to do if a specialist cannot complete its assignment.
The orchestrator should focus on coordination rather than doing the actual work. When an orchestrator starts performing detailed analysis or writing extensive content itself, that is usually a sign that a specialist is needed for that capability. The orchestrator's value is in the coordination, not the execution.
Orchestrators benefit from maintaining memory of the overall task progress. As specialists complete assignments, the orchestrator tracks what is done and what remains. This task-level state helps ensure nothing falls through the cracks and enables recovery if a specialist fails partway through.
Designing Specialists
Specialists should be focused and well-tooled for their particular capability.
A research specialist might have access to web search, document retrieval, and reading tools. Its prompt emphasizes finding relevant, credible sources and extracting key information. It does not need analysis tools or writing tools because those are different specialists' responsibilities.
An analysis specialist might have access to data processing and computation tools. Its prompt emphasizes identifying patterns, making comparisons, and producing structured insights. It receives information from research and produces structured analysis for writing.
A writing specialist might have access to formatting and content generation tools. Its prompt emphasizes clear communication, appropriate structure, and correct use of sources. It receives research and analysis and produces polished output.
Each specialist is simpler than a general-purpose agent would be. The focused scope makes prompts easier to write, behavior more predictable, and debugging more straightforward. When something goes wrong, you know which specialist to investigate based on what kind of error occurred.
Information Flow Between Agents
How information passes between agents significantly affects system behavior.
When the orchestrator delegates to a specialist, it provides the assignment context. This should include what the specialist needs to know to complete the task, but not everything the orchestrator knows. Passing too much context wastes tokens, risks confusing the specialist, and may include information irrelevant to the specific sub-task.
When a specialist completes its assignment, it returns a result to the orchestrator. This result should be structured and complete enough for the orchestrator to use effectively. A research specialist returns its findings in a format that analysis and writing specialists can process. An analysis specialist returns structured insights rather than raw data.
The orchestrator integrates results from multiple specialists into coherent outputs. This might involve summarizing across specialists, identifying how different results relate, or simply sequencing outputs appropriately. The integration step is where the orchestrator adds value beyond simple routing.
Isolating information flow to defined interfaces between agents makes the system easier to understand and debug. You can inspect what information crossed each boundary. If a specialist produced bad results, you can examine exactly what input it received.
Parallel Delegation
One benefit of hierarchical structure is enabling parallel execution. When the orchestrator has multiple independent assignments, it can delegate to several specialists simultaneously rather than waiting for each to complete before starting the next.
Consider a research task requiring information from three different areas. A sequential approach takes 3x as long as each search. Parallel delegation sends all three to research specialists at once. The orchestrator waits for all to complete, then proceeds with the combined results.
Not all delegations can be parallelized. If the analysis specialist needs research results, it cannot start until research completes. But within a phase - like gathering information from multiple sources - parallel execution significantly improves total task time.
The orchestrator does not need to manage the mechanics of parallel execution. The runtime handles running multiple sub-agents concurrently. The orchestrator simply expresses that these delegations are independent, and the infrastructure executes them efficiently.
Handling Failures
Sub-agents fail. They hit rate limits, misunderstand assignments, or produce unhelpful results. Hierarchical systems provide natural points to handle these failures.
When a specialist fails, the orchestrator receives the failure information instead of a successful result. The orchestrator can then decide how to proceed. Options include retrying the same specialist, trying a different specialist, adjusting the assignment and trying again, or proceeding without that particular result.
This centralized failure handling is simpler than distributed error handling would be. The orchestrator has context about the overall task and can make informed decisions about whether failures are recoverable and how to proceed. It can also communicate appropriately with users about delays or partial results.
Specialists themselves should fail cleanly rather than struggling indefinitely. If a specialist cannot complete its assignment after reasonable attempts, returning a clear failure is better than producing bad output or hanging. The orchestrator can handle clean failures; it cannot easily detect subtle quality problems in outputs.
Nesting and Recursion
Hierarchical delegation supports nesting - specialists can themselves be orchestrators with their own sub-agents. This enables complex task structures without overwhelming any single agent.
A top-level orchestrator might delegate to three specialists. One of those specialists might be an orchestrator for a subsystem of its own, coordinating two lower-level specialists. The structure reflects the natural hierarchy of the task.
Practical limits exist on useful nesting depth. Each layer adds latency and complexity. If you find yourself going more than two or three levels deep, consider whether the task decomposition is appropriate or whether some levels could be eliminated.
The same principles apply at each level. Orchestrators coordinate. Specialists execute. Information flows through defined interfaces. Failures propagate upward to handlers that have context to address them.
When Hierarchy Fits and When It Does Not
Hierarchical delegation works well for complex tasks with distinct phases, for work requiring genuinely different capabilities, and for situations where parallel execution provides significant benefit. Research projects, content creation pipelines, and data processing workflows are natural fits.
Hierarchy may be unnecessary for simple tasks that one agent handles well, for work without clear phases or capability boundaries, or when the overhead of coordination exceeds the benefit of specialization. Not everything needs to be a multi-agent system.
The question to ask is whether breaking into specialists provides genuine benefit. If each specialist is substantially better at its task than a generalist would be, hierarchy makes sense. If you are creating specialists just to have multiple agents, you are adding complexity without value.
For teams building multi-agent systems, inference.sh provides native support for hierarchical delegation. Sub-agents are simply another type of tool that orchestrators can call. The runtime handles parallel execution and result aggregation. You design the hierarchy and write the prompts; the infrastructure handles the mechanics.
Effective multi-agent systems are not about having many agents. They are about having the right structure for the work. Hierarchical delegation provides a structure that works reliably because it gives agents clear roles, defined interfaces, and explicit coordination.
FAQ
How do I decide how many specialists to create for a task?
Start by identifying genuinely distinct capabilities the task requires. If different parts of the task benefit from different tools, different prompts, or different models, those are candidates for separate specialists. A research phase that needs search tools is distinct from an analysis phase that needs computation tools. Avoid creating specialists for every minor sub-task - the coordination overhead will outweigh the benefits. Aim for specialists that each handle a meaningful chunk of work. If you find specialists that are rarely used or always used together, consider merging them. The right number is usually smaller than you initially think.
Should sub-agents share memory with the orchestrator?
It depends on the task and how specialists need to interact. For independent specialists that receive all necessary information in their assignment and return complete results, separate memory avoids pollution between specialists. For specialists that build on each other's work over multiple interactions, shared memory can enable continuity. A middle path is scoped memory - the orchestrator maintains task-level memory that it can selectively share when delegating. Specialists work independently during their assignment, but the orchestrator integrates learnings into memory between delegations. Start with less memory sharing and add it if you find specialists lack context they need.
How do I debug when something goes wrong in a multi-agent system?
Hierarchical systems are easier to debug than flat collaboration because information flows through defined points. Start by identifying which specialist produced the problematic output. Examine the input that specialist received - was the assignment clear and complete? Examine the output - did the specialist accomplish what was requested? If the assignment was good but the result was bad, the specialist needs improvement. If the assignment was unclear or missing information, the orchestrator's delegation logic needs work. Trace problems through the hierarchy, narrowing down at each level. Good observability tools that show the information at each boundary make this much easier than trying to reconstruct flows from logs.