Coordinator–Worker–Sub-agent Hierarchy

• Coordinator–worker–sub-agent hierarchy is a structured model in hierarchical multi-agent systems that decomposes global tasks into coordinated subtasks, enabling scalable oversight and local autonomy.
• It employs a multi-tier communication protocol where top-level coordinators assign tasks to workers that further delegate to specialized sub-agents, ensuring efficient task execution and cost optimization.
• Empirical evaluations show significant improvements in task success rates, resource efficiency, and scalability across applications from distributed AI to industrial automation.

A coordinator–worker–sub-agent hierarchy is a canonical organizational structure in hierarchical multi-agent systems (HMAS) designed to unify global oversight with local autonomy and scalable task decomposition. This architecture features three distinct tiers: the coordinator (orchestrator/manager) at the top, responsible for problem decomposition and strategy; intermediary workers managing subtasks and local orchestration; and sub-agents executing fine-grained, specialized operations or tool invocations. The model is foundational to both classical distributed artificial intelligence and modern LLM-agent frameworks, offering a principled approach to managing complexity, ensuring controllability, and optimizing performance–cost trade-offs across a wide range of domains (Ruan et al., 3 Feb 2026, Moore, 18 Aug 2025, Li et al., 11 Nov 2025, Xu et al., 4 Dec 2025, Ren et al., 14 Jan 2026).

1. Formal Structure and Agent Abstractions

In the most general formalism, each agent in the hierarchy is parameterized by a tuple that captures its role and executable context. AOrchestra (Ruan et al., 3 Feb 2026), for example, models every agent—coordinator or sub-agent—as a four-tuple: $A = (I, C, T, M),$ where $I$ is the instruction (objective, success criteria), $C$ is the working context (task-specific memory), $T$ is the permissible tool set (APIs, primitives), and $M$ is the underlying model (typically an LLM or policy network). Each agent implements a stochastic policy $\pi_M(a\,|\,I, C, T)$ to select its next action based on these parameters.

The Worker tier serves as an abstraction boundary: it receives abstract tasks from the coordinator, partitions or adapts them into concrete subtasks, aggregates bottom-up reports from sub-agents, and enforces localized policies or error-handling strategies. Sub-agents, typically numerous and specialized, respond to direct assignments from their assigned worker, executing atomic tool calls, computations, or environmental actions.

The formal structure is rigorous across modern implementations. In SciAgent (Li et al., 11 Nov 2025), the hierarchy extends recursively: domain-specialized Worker ensembles instantiate dynamic reasoning pipelines by orchestrating multiple sub-agents (e.g., Generator, Verifier, Summarizer), each handling modality-specific or pipeline-specific subtasks.

2. Coordination and Communication Protocols

Coordinator–worker–sub-agent hierarchies are defined by structured, protocol-driven communication patterns—both in control (task delegation) and data exchange (result aggregation). The most common control flow is tree-structured, with strict top-down and bottom-up message paths: coordinator→worker→sub-agent downwards for instruction; status, bids, results, or alerts upwards.

AOrchestra employs a JSON-based delegation protocol: the orchestrator issues "Delegate" actions, packaging the (I, C, T, M) tuple, to dynamically instantiate sub-agents. Sub-agents, upon completion or timeout, return typed observations (status, summary, artifacts, logs), which the orchestrator merges back into the global context for planning the next step (Ruan et al., 3 Feb 2026).

SciAgent leverages a blackboard memory and a formal message schema. Each message specifies sender, receiver, pipeline stage, message type (request/response/feedback), modality, data payload, and metadata. This schema is critical for parallel or sequential agent pipelining, cross-modal integration, and error localization (Li et al., 11 Nov 2025).

In reinforcement learning-based frameworks, worker–sub-agent exchanges often use the contract net protocol, hierarchical Q-learning, or subgoal communication by augmenting observations with coordinator-sampled targets (Moore, 18 Aug 2025, Ahilan et al., 2019). In all cases, explicit temporal and structural layering guarantees controllability and stable credit assignment.

3. Task Decomposition, Delegation, and Execution Flow

A central feature of this hierarchy is recursive or multi-stage task decomposition. Coordinators operate over global (user-level) objectives, decomposing them into sequences or graphs of worker-level tasks—each further subdivided into sub-agent-executable atomic units (Masters et al., 2 Oct 2025, Li et al., 21 Nov 2025). Strategies include:

• Rule-based decomposition: Coordinator parses natural language goals or structured intent into explicit task graphs or dependency DAGs (Zhou, 28 Oct 2025, Li et al., 21 Nov 2025).
• Dynamic pipeline assembly: Based on domain/classification heuristics, the coordinator routes each problem to a domain-specialized worker, which configures a stagewise execution pipeline, instantiating and scheduling sub-agents as needed (Li et al., 11 Nov 2025).
• Learning-based decomposition: Reinforcement or preference learning optimizes the planner (top-level agent) to output efficient decompositions and adaptively replan upon failures (Hu et al., 29 May 2025).
• On-the-fly sub-agent creation: Systems such as AOrchestra synthesize sub-agents dynamically for each atomic subtask, reducing static engineering and supporting plug-and-play specialization (Ruan et al., 3 Feb 2026).

Execution proceeds in a hybrid of top-down assignment/bottom-up aggregation, with built-in quality-control and re-planning. For example, Agentic Lybic (Guo et al., 14 Sep 2025) maintains a finite-state machine governing all control logic, routing tasks, and verifying outcomes at each tier.

4. Performance–Cost Trade-offs and Adaptivity

A critical strength of the coordinator–worker–sub-agent hierarchy is the ability to assign each subtask the minimal set of capabilities, tools, and computational resources required. AOrchestra exposes the model choice $M_t$ (e.g., choosing between a strong expensive model or a cheaper model for each sub-agent) as a delegation parameter, balancing accuracy versus resource cost. The orchestrator optimizes: $$\max_\pi \mathbb{E}\left[\mathbf{1}\{\text{success}\} - \lambda \cdot \mathrm{Cost}(\tau)\right],$$ where $\lambda$ controls the cost preference (Ruan et al., 3 Feb 2026). Pareto-efficient strategies emerge by switching models or tool granularities, allowing the system to approach optimal accuracy-cost frontiers.

Reinforcement or preference learning (e.g., OWL’s DPO objective) can further optimize decomposition policies for cross-domain generalization and minimal retraining cost (Hu et al., 29 May 2025). Empirical results across benchmarks (GAIA, Terminal-Bench, SWE-Bench) confirm significant gains in both raw task success and resource efficiency.

5. Architectural Variants and Industrial Applications

While the core three-tier structure is robust, significant architectural variety exists:

System	Coordinator Layer	Worker Layer	Sub-agent Layer
AOrchestra (Ruan et al., 3 Feb 2026)	Orchestrator	On-the-fly spawned sub-agent (worker)	Fine-grained tool executors
SciAgent (Li et al., 11 Nov 2025)	Coordinator Agent	Domain Worker System (Math, Physics, etc.)	Generator, Verifier, etc.
Workforce (Hu et al., 29 May 2025)	Planner	Coordinator agent managing assignments	Domain-specific Worker agents
OrchVis (Zhou, 28 Oct 2025)	Goal/Task Planner	Goal-Level Manager (per sub-goal)	Specialized executors
CORAL (Ren et al., 14 Jan 2026)	Info-Flow Orchestrator	LLM-based Worker Agents (star topology)	Callable tool wrappers
Feudal Multi-Agent (Ahilan et al., 2019)	Manager	Worker agents (policy-learners)	Policies over primitive actions

In industrial settings, the pattern is pervasive: in smart grid systems, a global coordinator sets economic targets, workers manage microgrids or substations, and sub-agents directly actuate inverters or appliances (Moore, 18 Aug 2025). In oil and gas, coordinators allocate production windows, with rig-level agents managing drilling schedules and sub-agents controlling physical actuators or sensors. These implementations have documented reductions in communication overhead (up to 35%), improved stability (22%), and dramatic improvements in fault response and global efficiency.

6. Scalability, Autonomy, and Open Challenges

Coordinator–worker–sub-agent hierarchies offer substantial scalability benefits by localizing decision loops and limiting global message complexity to $\Theta(n + \sum_w m_w)$ for $n$ workers each with $m_w$ sub-agents, as opposed to flat architectures’ $\Theta(N)$ scaling (Moore, 18 Aug 2025, Ahilan et al., 2019). Local reward functions, peer-to-peer sub-agent exchanges, and decentralized execution are often achieved, but global optimality can become misaligned if intrinsic rewards are not carefully tuned. Strategies for reconciling local autonomy with global utility include hierarchical reinforcement learning, explicit re-planning thresholds, and multi-objective optimization (Masters et al., 2 Oct 2025).

Outstanding challenges include explainability of hierarchical decisions, interactive conflict resolution, governance and constraint adherence (especially for human–AI teams), and robust adaptation to dynamic agent populations or evolving stakeholder preferences (Masters et al., 2 Oct 2025, Zhou, 28 Oct 2025). Furthermore, integrating LLM agents safely and efficiently into such frameworks is an active area of investigation.

7. Empirical Results and Benchmarks

Empirical evaluation across recent systems demonstrates that coordinator–worker–sub-agent hierarchies yield substantial accuracy, reliability, and cost advantages:

• AOrchestra: pass@1 = 71.62% (16.28% relative improvement over previous SOTA) with unsupervised configuration; further improvement and cost reduction with fine-tuned orchestrator (Ruan et al., 3 Feb 2026).
• SciAgent: achieves or surpasses human gold-medalist performance on mathematics and physics Olympiad benchmarks, demonstrating highly generalizable pipeline construction (Li et al., 11 Nov 2025).
• Agentic Lybic: state-of-the-art 57.07% success on OSWorld desktop automation, outperforming concurrent systems across multiple application domains (Guo et al., 14 Sep 2025).
• CORAL: outperforms workflow-based MAS by 8.49 pp on GAIA pass@1 with matched token usage, especially on edge-case-rich tasks due to adaptive, centralized A2A orchestration (Ren et al., 14 Jan 2026).

These results collectively establish the coordinator–worker–sub-agent hierarchy as a foundational pattern for scalable, efficient, and general agentic orchestration across both research and industrial domains.