Dual-Agent Prompting Architecture

• Dual-agent prompting architecture is a multi-agent system where two specialized LLM agents, each with distinct roles, collaborate through structured communication protocols.
• It uses codified exchange methods such as YAML, JSON, or pseudocode to deliver modularity, token efficiency, and verifiable planning across diverse tasks like reasoning, code synthesis, and multimodal bridging.
• Empirical benchmarks highlight significant improvements, including up to 87% token reduction and higher accuracy in logical consistency and task completion versus single-agent setups.

A dual-agent prompting architecture is a multi-agent system in which two specialized LLM agents interact through structured communication protocols to achieve enhanced reasoning, planning, annotation, or generative performance. Unlike single-agent or monolithic prompting schemes, dual-agent architectures explicitly separate distinct cognitive or operational responsibilities—such as planning vs. execution, proposal vs. critique, or text vs. image synthesis—thereby improving modularity, token efficiency, logical consistency, and solution quality. These systems leverage formal interfaces (structured pseudocode, YAML, JSON, or domain-specific markups) to enable precise, verifiable, and composable reasoning, and are empirically validated to outperform single-agent and naive multi-agent prompting on diverse benchmarks including reasoning, procedural planning, code synthesis, and knowledge extraction.

1. Conceptual Foundations and Agent Role Definitions

Dual-agent prompting architectures build on the core insight that separating sub-tasks between two LLM agents with orthogonal or complementary functions enables both specialization and structured collaboration. Representative roles include:

• Planning–Orchestration: One agent (e.g., PlanAgent) synthesizes a modular, typed pseudocode plan given a user goal; the other (TaskAgent) executes steps, manages external environment interaction, and mediates feedback (Yang et al., 4 Jul 2025).
• Producer–Verifier: An “Analyst” decomposes tasks via explicit chains of thought; a “Verifier” inspects the reasoning trace and answer, amends errors, and produces a final output (Chen et al., 26 Apr 2024).
• Explorer–Evaluator: In extraction or annotation, an Explorer agent proposes entity/relationship annotations and supporting evidence; an Evaluator critiques outputs for rationality and completeness, enforcing iterative improvement (Hu et al., 20 Aug 2024).
• Multimodal Bridging: A language agent generates textual plans; an image agent synthesizes aligned visual steps, with both modalities refining each other through structured text-image bridges (Lu et al., 2023).
• Teacher–Learner (in code synthesis): One agent detects errors and suggests corrections; two specialized learners apply fixes to code and test artifacts in parallel (Mi et al., 15 Dec 2024).

Communication is strictly codified: no free-form, unstructured natural language is exchanged. Instead, agents utilize fixed-format pseudocode, YAML, or JSON for plans, error traces, and feedback, and may perform explicit hand-offs of intermediate products such as subplans, critique traces, or context vectors.

2. Interaction Protocols and Structured Prompt Design

Communication protocols in dual-agent architectures are designed to maximize interpretability, verifiability, and token efficiency. Typical features across leading frameworks include:

• Codified Exchange Schema: System prompts specify precise agent roles, tool interfaces, and variable types—for instance, YAML system blocks describing available APIs and agent cycles (Yang et al., 4 Jul 2025).
• Pseudocode and Skeleton Templates: User queries are transformed into typed, modular pseudocode skeletons for the planner agent, who emits complete plans in executable code blocks.
• Structured Feedback Loop: The orchestrator executes each plan step, collects structured observations or error traces (e.g., JSON with failed steps and error messages), and returns these as formal inputs for plan revision (Yang et al., 4 Jul 2025).
• Multimodal Bridging: Text-to-image (T2I) and image-to-text (I2T) bridges facilitate bidirectional grounding between modalities in procedural planning tasks (Lu et al., 2023).
• Explicit Consensus/Conflict Mechanisms: In architectures focused on logical consistency, a quadratic consensus subproblem is solved in prompt-template space when agents disagree, ensuring convergence to consistent outputs (Dhrif, 30 Sep 2025).
• Turn-Taking and Cooperative RL: In reinforcement settings, agents alternate token-level prompt composition steps, optimizing a shared reward while a centralized critic observes inter-agent context (Kim et al., 2023).

The following table summarizes exemplar agent roles and communication formats:

Architecture	Agent 1 Role	Agent 2 Role	Communication Format
CodeAgents (Yang et al., 4 Jul 2025)	PlanAgent (planner)	TaskAgent (orchestrator)	Typed pseudocode, YAML, JSON
CoMM (Chen et al., 26 Apr 2024)	Analyst	Verifier	Chain-of-thought, critiques
LLM-Duo (Hu et al., 20 Aug 2024)	Explorer	Evaluator	Structured prompt, JSON
TIP (Lu et al., 2023)	LLM (plan)	T2I-Image Agent	T2I and I2T bridges, captions

3. Formal Properties, Mathematical Guarantees, and Algorithms

Multiple frameworks provide formal guarantees and explicit mathematical descriptions of dual-agent interactions:

• Lipschitz and Contraction Analysis: Reasoning-Aware Prompt Orchestration defines agent states as $S_i(t) = (P_i(t), C_i(t), M_i(t))$ with updates $S_i(t+1) = f_i(S_i(t), S_j(t))$. The update mapping is $L$-Lipschitz; global convergence is guaranteed for step sizes $\alpha < \frac{1}{2L}$, with contraction rate governed by the spectral radius of the update Jacobian (Dhrif, 30 Sep 2025).
• Consensus Mechanism: Conflicting agent plans are resolved by minimizing a quadratic form:

$$c^* = \arg\min_c \left[ w_1 \|c - P_1\|^2 + w_2 \|c - P_2\|^2 + \lambda \|c - c_{\rm prev}\|^2 \right]$$

yielding closed-form $c^*$ to update both prompt templates (Dhrif, 30 Sep 2025).

• Cooperative Markov Game RL: MultiPrompter (Kim et al., 2023) models prompt construction as a two-agent Markov game, alternating token selection, optimizing joint expected return:

$$J(\theta^1, \theta^2) = \mathbb{E}_{x,y \sim p(\cdot|\theta^1, \theta^2)}\left[ \sum_{t=1}^T \gamma^{t-1} r(s_t, a_t) \right]$$

with centralized-critic actor-critic updates for both agents.

• Ontology-Guided Scheduling: LLM-Duo (Hu et al., 20 Aug 2024) employs a prioritized BFS traversal of an ontology $\mathcal{G}=(\mathcal{E},\mathcal{R},\mathcal{F})$ to sequence dual-agent annotation tasks, prioritizing nodes by out-degree/in-degree ratio to maximize annotation coverage and context propagation.

4. Empirical Performance and Evaluation Benchmarks

Dual-agent prompting architectures achieve superior empirical performance across benchmarks, as consistently quantified by accuracy, token reduction, logical consistency, and task completion rates.

• Token Efficiency and Modularity: CodeAgents (Yang et al., 4 Jul 2025) demonstrates input token reductions of $55\%$–$87\%$, output token reductions of $41\%$–$70\%$, and absolute planning gains of $+3\%$–$36\%$ over natural language prompting, with state-of-the-art $56\%$ success on VirtualHome (prior best: $40$–$54\%$).
• Logical Consistency: Reasoning-Aware Prompt Orchestration (Dhrif, 30 Sep 2025) reports a $23\%$ ROUGE-L improvement and $89\%$ task completion rate without context loss, empirically tying logical consistency to the prompt consensus mechanism.
• Reasoning Correction and Fusion: CoMM’s analyst–verifier pipeline yields measurable improvements in accuracy (ACC), agreement rate (AGR), analyst correction rate (CRT), and confidence calibration, outperforming single-agent chain-of-thought on complex science and moral reasoning (Chen et al., 26 Apr 2024).
• Multimodal Quality: TIP (Lu et al., 2023) achieves $61$–$64\%$ human preference rates for textual/visual informativeness, coherence, and plan accuracy versus unimodal baselines, with best-in-class automated WMD, ROUGE-L, and FID metrics.
• Code Synthesis Robustness: CoopetitiveV (Mi et al., 15 Dec 2024) delivers $>96\%$ pass@10 and $100\%$ syntax conformance on Verilog benchmarks, with significant gains over single-agent and self-repair methods.
• Annotation Quality: LLM-Duo (Hu et al., 20 Aug 2024) achieves $100\%$ accuracy and $86.4\%$ mention coverage in Intervention Recognition, with a $20$–$50$ point gain over RAG-only baselines.

5. Advantages: Token Efficiency, Modularity, Scalability, and Reasoning Quality

Dual-agent architectures, as exemplified by CodeAgents (Yang et al., 4 Jul 2025), consolidate several advantages over single-LLM or single-agent approaches:

• Token Efficiency: Codified prompts (typed pseudocode, skeletons) and minimal natural language reduce both API cost and context window usage, e.g., up to $87\%$ input and $70\%$ output token reductions.
• Modularity: Agents can be extended or exchanged by modifying system block configurations, not full prompt texts; responsibilities (planning, execution, verification) are isolated for verifiability and debugging.
• Scalability: New functions, tools, or agents are incorporated by compositional update to the protocol (e.g., YAML block extension or capability matrix update), not monolithic prompt retraining.
• Specialization: Dedicated reasoning paths (chain-of-thought, verification) and critic/planner split achieve higher solution quality, correct more errors, and provide robust coverage of complex solution spaces (Chen et al., 26 Apr 2024, Mi et al., 15 Dec 2024).
• Empirical Quality: Superior accuracy, logical consistency, and user preference on diverse domains—procedural planning, extraction, code, and reasoning—universally confirmed via controlled benchmarks.

6. Limitations, Open Questions, and Theoretical Implications

While dual-agent architectures yield pronounced gains, certain limitations and open theoretical questions remain:

• Scaling to Many Agents: In reasoning-aware orchestration, performance degrades beyond $10$ active transitions, and system memory grows linearly with agent state size; optimal trade-off remains open (Dhrif, 30 Sep 2025).
• Conflict Resolution Overhead: Iterated consensus and critique steps impose latency, suggesting further research on efficient distributed coordination mechanisms.
• Domain-Specific Engineering: While core abstractions are general, each application (planning, procedural guidance, code correction, ontology extraction) presently requires substantial domain adaptation of prompt structure, retrieval context, and feedback analysis.
• Reward and Critique Modeling: Experimental gains rest on handcrafted or loosely parameterized reward, correction, or fusion functions; more principled or adaptive formulations may further enhance robustness.
• Single-Agent Baseline Degeneration: Dual-agent methods outperform single-agent self-repair in both code and reasoning tasks, attributed to reduction in hallucination accumulation and error propagation (Mi et al., 15 Dec 2024), but understanding fundamental reasons for LLM self-correction failure remains an open area.

7. Synthesis and Field Impact

Dual-agent prompting architectures constitute a foundational design pattern for LLM-driven multi-agent systems—enabling compact, verifiable, and composable coordination across planning, reasoning, verification, and complex data extraction. Empirical results across reasoning, procedural, code, and knowledge extraction domains establish consistent, statistically significant gains in efficiency and accuracy. Ongoing research targets higher-agent scaling, automated role assignment, adaptive consensus, and intrinsic reward shaping, aiming to extend the modular, interpretable, and universally applicable dual-agent paradigm throughout multi-agent LLM orchestration research (Yang et al., 4 Jul 2025, Dhrif, 30 Sep 2025, Chen et al., 26 Apr 2024, Lu et al., 2023, Mi et al., 15 Dec 2024, Hu et al., 20 Aug 2024, Kim et al., 2023).