ProAgent: Proactive LLM Agents

• ProAgent is a family of LLM-centric architectures enabling proactive intent inference, adaptive planning, and dynamic multi-modal execution.
• It integrates Bayesian methods and cost-sensitive policies to orchestrate complex workflows, significantly reducing user effort and improving task outcomes.
• Despite advances in automation and integration, challenges remain in scalability, optimal policy learning, and balancing proactive behaviors with user preferences.

ProAgent refers to a family of LLM-centric agent architectures and frameworks designed for proactive, context-aware assistance, automation, and adaptive reasoning across domains including GUI-based information integration, process automation, multi-agent cooperation, user interface analytics, and formal mathematics. ProAgent systems share a central focus on leveraging LLMs for proactive behavior generation, intent inference, dynamic planning, and human-level decision-making, differentiating themselves from purely reactive or rule-based paradigms.

1. Proactive Agent Architectures: Principles and Taxonomy

The unifying principle of ProAgent systems is the transition from reactive LLM-based agents (which passively respond to explicit user instructions) to agents that autonomously anticipate user needs, orchestrate complex tasks, and proactively coordinate information acquisition or workflow steps. ProAgent instances differ in application, yet typically architect their decision-making pipeline around the following modules:

• Intent inference (Comprehension): Parsing user queries and context, often via Bayesian or language-model-driven latent state estimation.
• Planning and Subtask Generation: Formulating proactive, cost-sensitive plans using expected information gain, utility, or user intent coverage thresholds.
• Execution: Interacting with GUIs, APIs, or software systems, either shallowly or with recursive, multi-app and multi-step strategies.
• Integration and Response Synthesis: Fusing multimodal data (text, vision, metadata) into comprehensive responses.
• Personalization/Memory: Adapting subsequent interactions using learned or recorded user/session data.

A plausible implication is that this architectural decomposition enables high modularity, openness to integration across new domains, and direct adaptation to emerging LLM functionalities (Zhao et al., 26 Aug 2025).

2. ProAgent for Proactive Multidomain Information Integration

AppAgent-Pro (synonymously, "ProAgent" in the proactive-GUI context) is a prototypical instantiation. It extends traditional LLM-based GUI assistants by embedding:

• Latent need inference: Infers a posterior over user needs $\theta$ given utterance $u$ and history $h$ as $P(\theta|u,h) \propto P(u|\theta,h)P(\theta|h)$.
• Cost-sensitive policy optimization: Selects a planning policy by $$\pi^* = \arg\max_\pi \mathbb{E}_{s,a\sim \pi}[R(s,a) - \lambda C(a)]$$ balancing information gain and action cost.
• Deep multimodal execution: Recursive subquery decomposition and parallel interaction across multiple apps (YouTube, Amazon), with a mixture of OCR, metadata extraction, and GUI automation (via Android ADB).
• Personalization memory: Session traces and summaries feed into priors, accelerating future inference.

Evaluation highlights qualitative improvements over reactive agents: richer answers, fewer user prompts, and higher multimodal coverage. Pilot tests indicate user effort reductions of 30–50%, and ablation suggests that deep execution and personalization respectively contribute 25% and 15% improvements in coverage and task completion time. Limitations include current support for only two apps and reliance on heuristic rather than learned policies. Future directions call for broader app coverage, RL-optimized planning, and neural Bayesian intent inference (Zhao et al., 26 Aug 2025).

3. Agentic Process Automation and Workflow Intelligence

In Robotic Process Automation (RPA), workflows are static DAGs $(A, E)$. Agentic Process Automation (APA), as instantiated in ProAgent (Ye et al., 2023), generalizes this model:

• Workflow nodes can be LLM-driven agents: $$W_{\rm APA} = (A\cup\{\text{DataAgent}, \text{ControlAgent}\}, E)$$.
• Construction Orchestrator: Iteratively calls an LLM over function-signature prompts for action definition, implementation, workflow orchestration, and termination decisions.
• Dynamic Workflow Execution: LLM calls resolve high-level data processing goals (DataAgent) and branching decisions (ControlAgent) at runtime, typically implementing a ReACT loop to resolve uncertainty and delegate sub-decisions.

Proof-of-concept evaluations on n8n.io, integrating real-time GPT-4 calls, confirm high-accuracy dynamic data manipulation and action branching tasks, with 100% correctness over test scenarios and end-to-end latency ~8\,s per row. However, large-scale benchmarks and robustness to LLM hallucinations remain open challenges. The APA paradigm as instantiated by ProAgent demonstrates full agentic lifecycle offloading—workflow construction and execution—to LLM-based agents, suggesting applications well beyond traditional task automation (Ye et al., 2023).

4. ProAgent in Adaptive Cooperative and Collaborative Tasks

In the context of multi-agent cooperation, ProAgent (Zhang et al., 2023) deploys a decentralized, LLM-centric planning loop designed for zero-shot adaptation:

• State grounding: Converts environment states $s_t$ into natural language $\ell_t$.
• Intention inference: Updates Bayesian beliefs $b_t(I) = P(I_t = I|o_{1:t}, s_{1:t})$ about teammate latent intentions based on observed actions $o_t$, environmental state $s_t$, and Markovian dynamics.
• Skill selection: Chooses skills $\omega_t$ to maximize expected utility under the current belief over teammate intentions:

$$\omega^*_t = \arg\max_{\omega\in\Omega^0} \sum_{I\in\mathcal{I}} b_t(I) Q(s_t, \omega; I)$$

• Chain-of-Thought planning: LLM planner produces explicit reasoning traces and verifies skills via multi-round prompt protocols.
• Memory and interpretability: Retains all reasoning steps, analyses, and decisions for transparency and downstream correction.

Empirical evaluation in Overcooked-AI yields top performance in both AI–AI and AI–human cooperation, with over 10% average improvement over state-of-the-art baselines in reward. Modular design and zero-shot generalization are achieved through explicit belief and intent modeling, underlining the suitability of ProAgent for complex, cooperative MDPs unwilling to restrict themselves to rigid, pre-trained protocols (Zhang et al., 2023).

5. Data-Driven Induction of Proactive LLM Agents

A significant thread in ProAgent research is the systematic induction of proactive agent behavior through data-driven benchmarks and learning from human feedback (Lu et al., 16 Oct 2024). The ProactiveBench dataset (6,790 events, cross-domain) supports supervised and reward-model-based fine-tuning:

• Event monitoring/annotation pipeline: Merges real human activity logs, LLM-generated candidate predictions, and multi-annotator human evaluation of accept/reject labels.
• Reward model learning: Fine-tuning LLaMA-3.1-8B with a classification head, trained with BCE to align with human acceptance, achieves F1 = 91.80% on annotated entries.
• Proactive LLM fine-tuning: Training LLaMA-3.1-8B and Qwen2-7B to output structured proactive suggestions (prompted JSON), yielding Qwen2-7B-Proactive F1 = 66.47%—outperforming all closed- and open-source baselines in proactive assistance.
• Applications: IDEs, writing tools, scheduling, and email summarization, with reward model gating and feedback refinement to limit false positives.

Current limitations involve persistent high false-alarm rates and the absence of live user studies; planned advancements include RL-based fine-tuning for direct policy optimization and extension to multimodal and privacy-preserving contexts (Lu et al., 16 Oct 2024).

6. ProAgent as Proactive UI/Visual Analytics Assistant

ProAgent is also instantiated as an LLM-driven UI agent for visual analytics (VA) workflows (Zhao et al., 24 Jul 2025). The architecture emphasizes:

• Perception: Detecting user struggle via temporal (pauses), behavioral (repetition), and semantic (annotation inconsistency) signals.
• Reasoning: Inferring user intent with few-shot prompts (Onboard, Explore, Verify), then generating/ranking candidate suggestions using a composite score $$s_{\text{score}}(s_i;C) = \alpha \cdot \text{sim} + \beta \cdot u + \gamma \cdot \text{nov}$$.
• Acting: Non-intrusive suggestion presentation, visual guidance, and user-driven or agent-executed UI action, with full chain-of-thought transparency.
• Controllability: User-adjustable intervention threshold, action preview, accept/reject pipeline, and real-time feedback adaptation.

Empirical analysis shows significant user- and expert-level gains: improved task completion and accuracy (accuracy 0.78 $\to$ 0.95), adoption of all top agent suggestions, and high user satisfaction scores. Future work targets richer context summarization, multimodal perception, and adaptive proactivity modeling (Zhao et al., 24 Jul 2025).

7. Limitations and Future Directions Across ProAgent Systems

The ProAgent paradigm, across instantiations, demonstrates substantial gains in automation, context-awareness, and adaptivity, but faces common limitations:

• Domain specificity: Limited integration breadth (e.g., number of supported apps/systems).
• Policy optimality: Heuristic or supervised planning dominates; few systems implement RL-based or advanced Bayesian intent inference.
• User acceptance and over-proactivity: Balancing agent initiative with user preferences and minimizing interruption/fatigue.
• Evaluation limitations: Reliance on case studies or simulated pipelines versus extensive live benchmarking or user trials.
• Scalability and interpretability: High computation costs, need for formal validation (e.g., provenance frameworks), and traceability in critical scientific or business workflows.

Future agendas include reinforcement learning of planning policies, broader environment adaptation, formal provenance capture (as in PROV-AGENT frameworks), and tighter human–AI co-adaptation loops (Zhao et al., 26 Aug 2025, Ye et al., 2023, Zhao et al., 24 Jul 2025, Lu et al., 16 Oct 2024, Souza et al., 4 Aug 2025).

---

Key ProAgent papers: (Zhao et al., 26 Aug 2025, Ye et al., 2023, Zhang et al., 2023, Lu et al., 16 Oct 2024, Zhao et al., 24 Jul 2025, Souza et al., 4 Aug 2025)