SEAgent Framework: Modular, Evolving Agents

• SEAgent is a unified framework defined by clear modular decomposition and self-evolving agent behaviors for enhanced task mastery.
• The framework leverages evolutionary trajectory optimization via revision, recombination, and refinement, resulting in up to 55% performance gains.
• It employs autonomous experiential learning and alignment training to enable agents to adapt and improve in real-world software environments.

The SEAgent Framework denotes a set of unified design principles, architectural modules, and evolutionary learning strategies for constructing intelligent agent systems—specifically in the contexts of software engineering and autonomous computer use—through modular decomposition and self-evolving agentic behaviors. While originally motivated by the absence of modularity and terminological consistency in state-of-the-art LLM-based agent frameworks, SEAgent synthesizes advances in unified modeling ("LLM-Agent-UMF"), trajectory optimization, alignment training, and autonomous experiential learning. This architecture is distinguished by its clear separation of functional agent components, support for both passive and active agent cores, and emphasis on evolutionary mechanisms for continual improvement in reasoning and task mastery.

1. Architectural Foundations and Unified Agent Modeling

The SEAgent Framework conceptually aligns with the LLM-Agent-UMF paradigm, which specifies a separation between LLMs, tools, and the "core-agent"—the central orchestrator responsible for managing agent execution (Hassouna et al., 17 Sep 2024). The core-agent is segmented into five precise modules:

• Planning
• Memory
• Profile
• Action
• Security

This modularization enforces the Open–Closed Principle (OCP): system modules are open for extension but closed to modification, promoting future-proof agent construction. Core-agents are dichotomized into active and passive types, reflecting their degree of autonomy and authority over the information fusion process.

The SEAgent design thereby enables composite multi-agent architectures, wherein distinct characteristics of active and passive agents can be hybridized to address complex, multi-stage tasks. Such delineation allows seamless integration of additional modules (e.g., security or specialized planners) without refactoring established systems—a direct contrast to architectures demanding anticipatory modification ("clairvoyance") in advance of all possible future requirements.

2. Evolutionary Trajectory Optimization Mechanisms

A distinctive feature underlying recent SEAgent frameworks is the application of self-evolutionary mechanisms for multi-step reasoning trajectory optimization (Lin et al., 4 Aug 2025). The framework operates on full reasoning trajectories, denoted $\tau = (s_0, a_0, s_1, a_1, \ldots, s_n)$, rather than isolated actions.

Evolution occurs through three iterative operations:

• Revision: Analyzing and improving pilot trajectories based on internal reflection scores.
• Recombination: Cross-pollinating high-value trajectory fragments (crossover, transfer, restructuring) from multiple agents.
• Refinement: Evaluating and selecting solutions via multi-dimensional reward functions: $$\text{Reward}(t, T) = \alpha \cdot \text{TaskCompletion}(t, T) + \beta \cdot \text{ReasoningQuality}(t) + \gamma \cdot \text{Efficiency}(t)$$.

This evolutionary protocol transcends the search space of standard MCTS methods, promoting cross-trajectory inspiration and reducing suboptimal local convergence. The strategic selection mechanism retains diversity among elite solutions, resulting in improved outcomes on benchmarks such as SWE-bench Verified, with recorded gains up to 55% over prior open-source agent baselines.

3. Autonomous Experiential Learning and Curriculum Generation

The SEAgent Framework for Computer Use Agents (CUAs) introduces a self-evolving loop wherein agents autonomously master unfamiliar software environments via trial-and-error exploration (Sun et al., 6 Aug 2025). The architecture consists of three integral models:

• Actor Model $(\pi)$: The policy responsible for interacting with the software.
• World State Model $(\mathcal{M}_\text{state})$: A dense-captioning LVLM that evaluates stepwise GUI transitions, issuing feedback $\mathcal{J}$ of correct $(a_T)$ and failure $(a_F)$ actions.
• Curriculum Generator $(\mathcal{M}_\text{task})$: Dynamically produces task instructions $(\mathcal{I}_p)$, escalating in complexity, leveraging cumulative guidebooks $\mathcal{U}$.

Learning incorporates adversarial imitation (penalizing failed actions, $\mathcal{L}_\text{AI}$) and Group Relative Policy Optimization (GRPO) for successful actions:

$$A^{(i)} = \frac{r^{(i)} - \text{mean}(\{r^{(j)}\})}{\text{std}(\{r^{(j)}\})}$$

The total policy loss is weighted: $$\mathcal{L}_\text{total}(\pi(\theta)) = \mathcal{L}_\text{GRPO} + \gamma \cdot \mathcal{L}_\text{AI}$$, with reported optimal $\gamma = 0.2$. This framework enables autonomous exploration and skill discovery, eliminating reliance on human annotations and generating specialist agents whose knowledge can be distilled into stronger generalist models.

4. Modular, Extensible Design Principles

The SEAgent Framework's modular architecture is engineered to maximize extensibility, traceability, and maintainability (Hassouna et al., 17 Sep 2024). By precisely demarcating agent responsibilities (planning, memory, profile, action, security), researchers can implement or substitute modules without perturbing unrelated agent functions. In practice, this supports forward-compatible module addition—such as security or specialized reasoning—and the development of hybrid agent systems that combine features from both passive and active agents.

The adherence to OCP, as scrutinized in the context of use case model relationships, avoids pitfalls of system fragility requiring "clairvoyant" architectural anticipation. Empirical application to thirteen state-of-the-art agents demonstrates that this design paradigm aligns functional decomposition with software engineering best practices, facilitating sustainable integration of new capabilities.

5. Alignment Training and Real-World Task Adaptation

SEAgent incorporates alignment training methodologies to bridge the gap between pre-trained code generation models and the requirements of real-world engineering workflows (Zhang et al., 24 Mar 2025). Critical steps include:

• Collection and merging of agentic trajectories using systems such as SWE-Gym.
• Monte Carlo Tree Search–like construction of trajectory trees for fine-grained action scoring.
• Preference optimization (DPO loss):

$$\mathcal{L}_\text{DPO} = -\mathbb{E}_{\text{traj}\in\mathcal{D}}\left[ \log \sigma \left( \beta \cdot \log \frac{P(a_i^+)}{P_{\text{ref}}(a_i^+)} - \beta \cdot \log \frac{P(a_i^-)}{P_{\text{ref}}(a_i^-)} \right) \right]$$

This aligns agent decisions toward high-value actions, improves tool invocation reliability, and reduces decision loops. Evaluations on HumanEvalFix, SWE-Bench-Lite, and SWE-Bench-Verified show substantial improvements, exemplified by a 14B model’s resolve rate increase from 3.7% to 17.7% with minimal overhead.

6. Integration into Agent-Based Platforms and Practical Adoption

SEAgent methodologies inform agent-based software development platforms capable of automated coding, iterative refinement, and user-driven task completion. Integrated platforms leverage aligned LLMs as autonomous agents, navigating workflows that include prompt reading, tool selection, and cyclic code improvement. Empirical construction of applications—ranging from web interfaces (to-do lists, personalized homepages) to game engines—exhibits marked improvements in functional completeness and user experience, validated by human assessments.

A plausible implication is that modular SEAgent techniques can be transferred to broader agentic domains, such as digital workspace automation, adaptive user interfaces, and multi-agent coordination frameworks.

7. Future Prospects and Open Technical Challenges

Ongoing research suggests potential extensions of SEAgent to reinforcement learning policy search, embodied intelligence, continual learning systems, and even multimodal SIA frameworks (such as those innovated by Estuary (Lin et al., 20 Apr 2025)). Open technical challenges include scaling evolutionary mechanisms, elaborating reward formulations, and ensuring robust curriculum generation for diverse agent populations.

In summary, the SEAgent Framework represents an overview of modular agent modeling, evolutionary reasoning optimization, autonomous experiential learning, and alignment-centric training strategies. Its design principles, validated performance outcomes, and extensibility underpin a rigorous foundation for constructing future-proof, self-evolving intelligent agents in software engineering and autonomous computing environments.