Modular LLM-Agent Architecture

• Modular LLM-agent architecture is a design paradigm that decomposes complex reasoning into discrete, interacting modules for tasks like planning, error detection, and state evaluation.
• It employs iterative planning, tree search, and recurrent workflows to achieve near-optimal performance on tasks such as graph traversal and the Tower of Hanoi.
• Inspired by cognitive neuroscience, the architecture offers interpretability, scalability, and cross-domain adaptability while simplifying debugging and module replacement.

A modular LLM-agent architecture is a system-level design paradigm wherein the complex problem-solving and decision-making capabilities of LLMs are partitioned into discrete, interacting modules—each implementing a narrowly defined function such as planning, action generation, state evaluation, or monitoring. The architecture enables structured task decomposition, efficient resource management, and transferability across domains by orchestrating domain-specialized modules via recurrent and cooperative workflows. This approach draws on principles from cognitive neuroscience, such as the compartmentalized structure of the human prefrontal cortex, as well as established agent-system methodologies in reinforcement learning and hierarchical planning. The following sections survey the core tenets, components, mechanisms, empirical validations, generalization properties, and research implications of the modular LLM-agent paradigm (Webb et al., 2023).

1. Theoretical Foundations and Modular Decomposition

The modular LLM-agent architecture is motivated by findings from cognitive neuroscience and RL that multi-step, goal-directed reasoning is accomplished by a set of interacting processes: task decomposition, decision making, error detection, state prediction, value estimation, and ongoing orchestration. In this architecture, these processes are implemented as separate modules:

Module	Primary Function	Biological Analogue
TaskDecomposer	Decompose overall goal into subgoals	Anterior Prefrontal Cortex (aPFC)
Actor	Generate candidate actions	Dorsolateral PFC (dlPFC)
Monitor	Error detection and constraint checking	Anterior Cingulate Cortex (ACC)
Predictor	Forecast next states from actions	Orbitofrontal Cortex (OFC)
Evaluator	Heuristic value estimation of states	Orbitofrontal Cortex (OFC)
Orchestrator	Supervise progress/termination/sequencing	Anterior Prefrontal Cortex (aPFC)

Each module is instantiated as an LLM (potentially using different model sizes per module). The semantic and formal independence of modules enables clear responsibilities, debugging, and composability.

2. Interaction Protocols and Search Procedures

Modules interact according to iterative, recurrent planning regimes modeled as a combination of proposal-evaluation-revision and tree search:

• Action Proposal Loop: The Actor proposes a set of $B$ candidate actions $A = \{a_1, ..., a_B\}$ given the current state $x$ and active subgoal $z$. The Monitor validates these actions against task constraints, supplying feedback $e$ on violations. The Actor replenishes invalid actions until a valid set is available.
• Tree Search & Backpropagation: Predictor generates state forecasts $\tilde{x}$ for candidate actions. The Evaluator computes value heuristics $v$ at search leaves, typically estimating the distance to the goal. Tree search explores actions up to a fixed depth $L$, with value backpropagation selecting optimal moves.
• Orchestration & Subgoal Sequencing: The Orchestrator monitors progress on subgoals $z_1,\ldots,z_n$ produced by TaskDecomposer, terminating planning when final goal $y$ is achieved or after $T$ steps (Algorithm 3).

This modular, recursive execution enables effective multi-step reasoning and mitigates error propagation by incorporating explicit feedback and error-checking at each decision/validation interface.

3. Empirical Evaluation and Quantitative Results

The efficacy of the modular LLM-agent approach (denoted as MAP or LLM-PFC) has been empirically validated on several discrete planning and reasoning tasks:

Graph Traversal

• Valuepath: 100% problem-solving rate.
• Steppath: Near-perfect accuracy, including in Detour and Reward Revaluation variance tests.

Tower of Hanoi (ToH)

• Seven-fold improvement in success rate over zero-shot prompting.
• Outperformed in-context learning, chain-of-thought (CoT), and multi-agent debate (MAD) baselines.
• Solutions achieved near-optimal plan lengths with almost zero invalid moves in both in-distribution and OOD test sets.

Logistics Planning

• On a multi-modal transportation planning task [Valmeekam et al. 2023], MAP achieved a 31% success rate (vs. 7.5–10.5% for GPT-4 zero-shot and ICL).

Ablation studies established critical module dependencies: disabling the Monitor dramatically increased invalid action frequency, and omitting TaskDecomposer or tree search led to major reductions in overall accuracy.

4. Transferability, Efficiency, and Model Selection

The modular architecture enables flexible adaptation across multiple planning domains without task-specific fine-tuning. Notably:

• Generalization: MAP modules trained/tested on graph traversal, ToH, PlanBench, and NLP benchmarks (strategyQA) demonstrated cross-domain transfer.
• Model Size and Cost: Although the reference implementation uses GPT-4, several sub-tasks (e.g., Monitor, Predictor, Evaluator) could be feasibly replaced or jointly fine-tuned using smaller models such as Llama3-70B, enabling more cost- and compute-efficient deployments.
• Module Substitution: The architecture is compatible with substituting or augmenting single modules (e.g., rule-based hard-coded Monitors or heuristic Evaluators) for further efficiency gains.
• Search Parameterization: The computational complexity of the tree search is regulated by branching factor $B$ and depth $L$, both of which are adjustable for task complexity or resource constraints.

5. Architectural Scalability and Interpretability

Key advantages of this modular approach are:

• Interpretability: Each module’s function is explicit and mappable to an interpretable cognitive/algorithmic process.
• Controllability: Developers may debug, analyze, or upgrade individual modules without affecting the entire system.
• Scalability: The approach provides a foundation for scaling to more complex, real-world agentic settings. Modules can be specialized or parallelized—e.g., multiple Actors focusing on different action subsets—enabling hierarchical or distributed planning.
• Integration of Neuroscientific Insights: Emulating the compartmentalization of prefrontal cortex subregions forms a bridge between neurocognitive modeling and agentic AI, offering both engineering clarity and potential insights for computational neuroscience.

6. Research Implications and Limitations

The modular LLM-agent paradigm (as instantiated in MAP/LLM-PFC) establishes several research pathways:

• Blueprint for Future Systems: The architecture’s blueprint—decomposing agents into tightly specified modules with explicit IO contracts—will inform future iterations of LLM-based agentic systems, enabling plug-and-play, hybrid, or jointly optimized modules.
• Systematic Error Reduction: Explicit error-handling modules (Monitor) systematically reduce invalid moves and failure modes like looping or rule violation, issues observed in CoT and ToT baselines.
• Dynamic System 1/System 2 Fusion: Modules can be specialized for “fast” (heuristic, shallow, or reflective) or “slow” (tree search, evaluative, deliberative) processing, facilitating hybrid architectures.
• Computational Bottlenecks: Current implementations rely on multiple LLM API calls per decision step; future work is needed to reduce redundancy via joint/cached computation or end-to-end model distillation.

This approach does not guarantee automatic efficiency or optimality; achieving practical utility in highly complex, temporally extended domains will require further research into scaling, resource scheduling, and more advanced module learning/fine-tuning methods.

7. Conclusion

The modular LLM-agent architecture operationalizes multi-step planning and reasoning by composing specialized, recurrently interacting modules (TaskDecomposer, Actor, Monitor, Predictor, Evaluator, Orchestrator) that collectively outperform monolithic and prompt-based LLM approaches in planning, task-solving, and adaptability. This paradigm demonstrates improved accuracy, interpretability, and cross-task transfer. Its design—anchored in both neuroscience and algorithmic planning—provides a principled foundation for developing next-generation, robust, and systematically extensible LLM agents (Webb et al., 2023).