Coordinator–Worker–Sub-Agent Architecture

• Coordinator–Worker–Sub-Agent Architecture is a multi-agent framework that organizes tasks into three tiers, enabling clear separation of planning, execution, and verification.
• It dynamically orchestrates agent pipelines with decoupled planning, iterative feedback, and modular sub-agent instantiation to enhance reliability and cross-domain adaptability.
• Empirical validations in scientific reasoning, automation, and swarm robotics demonstrate improved performance metrics and scalable adaptability in complex tasks.

A Coordinator–Worker–Sub-Agent architecture is a hierarchical organization for multi-agent systems, in which a top-level coordinator agent interprets tasks, delegates to domain-specialized worker agents, and these workers further instantiate sub-agents responsible for granular execution steps. This pattern serves as an organizing principle for reliable, adaptive, and generalizable agentic reasoning across complex domains including scientific problem solving, real-world automation, software workflows, and embodied robotics. The key motivation is to enable modular decomposition, dynamic orchestration, parallelization, and robust error handling in multi-turn or long-horizon tasks.

1. Structural Hierarchy and Formal Abstraction

The architecture is universally structured in at least three tiers:

1. Coordinator (Meta Level): Central entity receiving the initial problem or goal, performing domain/difficulty classification, and devising high-level plans or decomposition strategies.
2. Worker (Domain/Execution Level): Ensembles that translate coordinator plans into specific pipelines, invoking and managing sub-agents with specialized roles.
3. Sub-Agent (Atomic/Task Level): Micro-agents with narrowly defined capabilities (e.g., symbolic deduction, code execution, perception, verification), invoked on demand within the Worker pipeline.

A rigorous abstraction used in systems such as AOrchestra (Ruan et al., 3 Feb 2026) models each agent (main or sub) as a compositional four-tuple: $\Phi = \langle I,\, C,\, T,\, M \rangle$ where $I$ is the instruction, $C$ is curated context, $T$ is the set of tools, and $M$ is the specific model to run.

The control logic typically proceeds in alternating orchestrate–execute phases, where the coordinator delegates via $\mathrm{Delegate}(\Phi_t)$, sub-agents process tasks using only their local context and tools, and structured results are fed back for possible replanning.

2. Control Flow, Data Flow, and Orchestration Algorithms

Task execution in this architecture is broadly governed by the following principles:

• Decoupling of Planning and Execution: Strategic decomposition and routing are handled separately from execution, enhancing modularity and enabling cross-domain transfer (Hu et al., 29 May 2025).
• Dynamic Pipeline Assembly: Workers can instantiate sub-agent pipelines conditionally, assembling chains such as Generator → Reviewer → Improver until predefined convergence criteria are met (e.g., $\#\mathrm{PASS} \ge N$) (Li et al., 11 Nov 2025).
• Formal FSM-based Orchestration: Certain implementations (e.g., Agentic Lybic (Guo et al., 14 Sep 2025)) operate as finite-state machines with explicit state sets, triggers, and deterministic transition functions. Each atomic step transitions the global system state $S_t$ based on the latest action and observation, enabling adaptive replanning and continuous quality control.

Typical control and data flows are schematized as:

Coordinator: interpret input → decompose → assign Worker Worker: assemble sub-agents → manage pipeline/execution → aggregate feedback Sub-Agent: execute atomic action → emit result/verdict

All message passing is typically structured (e.g., JSON objects, typed communication channels), with well-defined role-tags and content schemas for automatability and traceability (Li et al., 11 Nov 2025, Nazzari et al., 2 Oct 2025). Many systems employ explicitly asynchronous, non-blocking interaction to maximize throughput and avoid bottlenecks (Chen et al., 2024).

3. Specialization, Role Assignment, and Adaptivity

Worker and sub-agent assignments are highly flexible:

• Domain- and Difficulty-Specific Routing: SciAgent (Li et al., 11 Nov 2025) routes problems to category-specific Workers (e.g., Math Olympiad Worker), with domain and difficulty inferred via classifier heads on LLM embeddings.
• Automated Sub-Agent Creation: Systems such as AOrchestra (Ruan et al., 3 Feb 2026) and CORAL (Ren et al., 14 Jan 2026) instantiate sub-agents dynamically as tool- or objective-specific executors, guided by context, tool availability, and cost–performance trade-off.
• Plug-and-Play Modularity: By standardizing agent interfaces (e.g., $\Phi$-tuple or API schemas), novel worker/sub-agent classes can be integrated without modifying orchestration logic or requiring end-to-end retraining.

Empirical evidence demonstrates that this modular specialization enhances generality, enabling cross-domain plug-and-play adaptability (e.g., web, software engineering, terminal automation) without architectural modifications (Hu et al., 29 May 2025, Ruan et al., 3 Feb 2026).

4. Verification, Quality Control, and Feedback Loops

Verification and convergence protocols are foundational:

• Iterative Feedback: Sub-agent outputs are reviewed, critiqued, and potentially revised multiple times per cycle. Feedback loops continue until explicit validation criteria are met (e.g., correct derivation, test passing) (Li et al., 11 Nov 2025).
• Automatic Gating: Quality gates, as in Agentic Lybic (Guo et al., 14 Sep 2025), periodically compare system state, subtask progress, and target achievement based on similarity and progress metrics, proactively triggering replanning, supplemental knowledge acquisition, or human handoff.
• Conflict Resolution: Advanced frameworks (e.g., OrchVis (Zhou, 28 Oct 2025)) apply formal verification metrics ($V_{\text{hard}}, V_{\text{soft}}$) to goals, detect task or constraint conflicts, and mediate either via automated or human-in-the-loop replanning strategies.

These mechanisms are critical to robust error recovery, minimizing failed executions, and maintaining correctness guarantees over complex, interdependent subtasks.

5. Empirical Validation Across Domains

The Coordinator–Worker–Sub-Agent pattern is empirically validated across a spectrum of demanding tasks:

• Scientific Reasoning: SciAgent attains or surpasses human gold-medalist performance across mathematics, physics, and chemistry Olympiad benchmarks, operationalizing domain and reasoning generality (Li et al., 11 Nov 2025).
• Long-Horizon Automation: Agentic Lybic achieves state-of-the-art in 50-step desktop workflows by combining FSM-based orchestration with continuous quality gating (Guo et al., 14 Sep 2025). AOrchestra attains a 16.28% improvement over baselines on GAIA, Terminal-Bench, and SWE-Bench (Ruan et al., 3 Feb 2026).
• Agentic Software Engineering: OWL demonstrates open-source SOTA for general multi-agent assistance, surpassing proprietary baselines through modular plug-in workers and RL-optimized planning (Hu et al., 29 May 2025).
• Decentralized Embodied Collaboration: S-Agents’ tree-of-agents structure yields reduced time cost and increased task reliability in open-ended Minecraft tasks, compared to flat or cyclic multiagent graphs (Chen et al., 2024).
• Robotic Swarm Coordination: TACOS generalizes to multi-UAV control via natural language, showing that LLMs can interpret, plan, and coordinate swarm missions through discrete point-to-point APIs managed by layered supervisor modules (Nazzari et al., 2 Oct 2025).

A synopsis of empirical metrics and contexts is tabulated below:

System	Domain	Benchmark/Setting	Peak Reported Accuracy/Performance
SciAgent	Olympiad Science	IMO, IMC, IPhO, CPhO, HLE	100/100 (IMC), 36/42 (IMO), 25.0/30.0 (IPhO)
AOrchestra	Automation, DevOps	GAIA, SWE-Bench, Terminal	71.62% pass@1 (Gemini-3-Flash)
OWL (Workforce)	Task Automation	GAIA	69.70% (Claude-3.7-Sonnet)
Agentic Lybic	Desktop Automation	OSWorld	57.07% (50 steps)
S-Agents	Minecraft	Resource Build/Collect	ToA(3) cuts time up to 50% vs. flat org
TACOS	Multi-drone Swarm	Real-world UAV	Empirical; qualitative reliability

6. Design Principles, Benefits, and Limitations

Collections of design principles are unified around:

• Tiered Separation of Concerns: Delegation, execution, and verification are assigned to orthogonal subsystems, enabling fault isolation, self-diagnosis, and clear scaling bounds.
• Dynamic Orchestration: Modular structure allows dynamic pipeline assembly, adaptive role allocation, and error-triggered replanning.
• Framework-Agnostic Extensibility: Standardized context/tool/model abstractions (as in AOrchestra’s four-tuple) decouple orchestration from implementation, supporting heterogeneous models and subsystems (Ruan et al., 3 Feb 2026).
• Asynchronous, Non-Obstructive Collaboration: Autonomous agents act in parallel; no global round synchronization is required, minimizing resource underutilization and “slowest-agent” bottlenecks (Chen et al., 2024).

However, limitations include potential single-coordinator bottlenecks, dependency on the reliability of external tools, system complexity in prompt and agent design, as well as possible issues with scaling to domains requiring extreme real-time response or ultra-domain-specific toolchains.

7. Research Implications and Future Directions

The Coordinator–Worker–Sub-Agent architecture has catalyzed a shift from handcrafted, brittle workflows to principled, modular, and learning-enabled agentic paradigms. Notable implications:

• Reduced Human Engineering: Automating both agent instantiation and context/tool curation (e.g., AOrchestra, S-Agents) minimizes manual workflow design and prompt engineering, and supports in-situ adaptation.
• Modular Cross-Domain Generalization: Separation of planning and execution enables robust transfer across tasks and toolkits, supporting continuous system evolution (Hu et al., 29 May 2025).
• Efficient Training Under Hard Constraints: Lightweight coordination heads (e.g., Trinity’s sep-CMA-ES optimization) outperform RL and imitation learning baselines under extreme budget and black-box settings (Xu et al., 4 Dec 2025).
• Formal Correctness and Transparent Oversight: Architectures such as OrchVis instrument formal verification metrics, conflict analysis, and support interactive human replanning, contributing to transparent and safe AI orchestration (Zhou, 28 Oct 2025).

Active research explores models for end-to-end trainable coordination, fine-grained context folding, plugin-based tool ecosystems, and federated or multi-root scaling. Addressing tool reliability, quantifying communication/latency trade-offs, and automating domain-specific worker role generation remain open frontiers.