The Rise of Adaptive Systems: Can AI Agents Build Self-Improving Software?

Author: Denis Avetisyan

A new framework, POLARIS, explores how multi-agent AI can move beyond reactive self-adaptation to systems that proactively reason about and optimize their responses to changing conditions.

The POLARIS framework organizes a system into three interconnected layers, incorporating adaptation loops to facilitate dynamic responses and maintain systemic coherence.

This review details POLARIS, a system leveraging large language models and multi-agent reasoning to engineer self-adaptive software capable of continual learning and improved performance.

Traditional self-adaptive systems struggle with the increasing complexity and uncertainty of modern software ecosystems, often reacting to change rather than proactively anticipating it. This paper introduces POLARIS: Is Multi-Agentic Reasoning the Next Wave in Engineering Self-Adaptive Systems?, a novel framework leveraging multi-agentic reasoning and meta-learning to move beyond reactive adaptation. POLARIS enables systems to not only learn from experience but also reason about and continuously evolve their adaptation strategies, demonstrating consistent performance gains over state-of-the-art baselines. Could this represent a paradigm shift towards a new era of self-adaptation, where systems autonomously refine their ability to thrive in dynamic environments?

Beyond Simple Reaction: Towards Intelligent System Adaptation

Conventional self-adaptive systems, frequently architected around the Monitoring, Analysis, Planning, Execution, and Knowledge (MAPE-K) model, often falter when confronted with genuinely dynamic and unpredictable environments. While effective within pre-defined operational boundaries, these systems primarily react to changes detected through monitoring – triggering pre-programmed responses based on established thresholds. This reactive nature proves insufficient when facing novel situations or gradual drifts outside of anticipated parameters. The inherent rigidity of rule-based adaptation struggles to cope with the combinatorial explosion of possibilities present in complex systems, leading to performance degradation or outright failure as unforeseen interactions emerge. Consequently, systems designed solely on reactivity demonstrate limited resilience and an inability to gracefully handle the inherent uncertainty of real-world deployments, highlighting a crucial need for more proactive and intelligent approaches.

Current self-adaptive systems often function as reactive entities, perpetually responding to stimuli based on explicitly programmed rules and predetermined thresholds. While effective within constrained parameters, this approach proves increasingly inadequate when confronted with genuinely novel situations or unpredictable environmental shifts. The reliance on pre-defined responses creates a bottleneck, hindering the system’s capacity to anticipate problems before they arise or to gracefully navigate conditions outside its programmed scope. Essentially, these systems are excellent at solving known issues, but lack the foresight to prevent them, limiting their robustness and long-term viability in truly dynamic environments. This reactive nature necessitates a move toward intelligent systems capable of learning and predicting, rather than simply responding to events as they unfold.

Current self-adaptive systems often function as reactive entities, responding to detected anomalies based on pre-programmed logic; however, increasingly complex environments demand more than simple reaction. A fundamental shift is occurring toward systems equipped with the capacity for proactive intelligence-systems that don’t just respond to ‘what is’, but reason about ‘what might be’. This necessitates integrating techniques from machine learning, predictive modeling, and knowledge representation to enable systems to learn from past experiences, anticipate future states, and adjust behavior accordingly. Such a transition moves beyond merely mitigating issues as they arise, and instead focuses on preventing them, optimizing performance, and fostering resilience in the face of unforeseen challenges. The ultimate goal is to create systems capable of not just surviving, but thriving, in dynamic and unpredictable conditions.

POLARIS leverages a fast controller and meta-learner to achieve robust performance.

POLARIS: A Layered Architecture for Reasoning and Adaptation

POLARIS differentiates itself from conventional self-adaptive systems through its layered architecture, designed to facilitate complex cognitive functions. This architecture incorporates distinct layers dedicated to reasoning, knowledge management, and long-term learning, enabling the system to not only react to immediate changes but also to build and utilize accumulated experience. The reasoning layer employs symbolic and sub-symbolic methods for problem-solving and planning. The knowledge management layer organizes and maintains a structured representation of the system’s understanding of its environment and itself, utilizing techniques such as knowledge graphs and semantic networks. Finally, the long-term learning layer implements mechanisms for consolidating experiences, refining models, and improving future performance, incorporating methods like reinforcement learning and meta-learning to enable continuous improvement beyond simple reactive adaptation.

POLARIS utilizes an Agentic AI approach, structuring its functionality around two core agents: the Reasoner and the Meta-Learner. The Reasoner agent is responsible for deliberative problem-solving, employing knowledge and inference to analyze situations and formulate plans. The Meta-Learner agent focuses on strategic adaptation, monitoring the Reasoner’s performance and adjusting its operational parameters to optimize long-term learning and improve future reasoning capabilities. This agentic division of labor enables POLARIS to not only respond to immediate challenges but also to proactively refine its internal processes based on observed outcomes and evolving environmental conditions.

POLARIS employs two distinct adaptive loops: the Reactive Stabilization Loop and the Proactive Adaptation Loop. The Reactive Stabilization Loop functions as an immediate failure recovery mechanism, triggering responses to critical system errors or performance degradation without requiring extensive analysis. Conversely, the Proactive Adaptation Loop utilizes reasoned predictions generated by the Reasoner agent to anticipate potential issues or opportunities. This loop enables the system to make preemptive adjustments to its configuration or strategy, optimizing performance and preventing failures before they occur. The distinction lies in the former’s responsiveness to existing problems and the latter’s anticipatory, prediction-driven approach to system maintenance and enhancement.

Consistent POLARIS performance across multiple runs demonstrates minimal variability in its configuration.

Reasoning with Knowledge: The Power of Deliberative Analysis

The Reasoner Agent employs Large Language Models (LLMs) as its core analytical engine, processing data retrieved from both the Knowledge Base and the World Model. Specifically, it utilizes Chain-of-Thought Prompting, a technique that encourages the LLM to articulate its reasoning process step-by-step before arriving at a conclusion. This method facilitates a more transparent and verifiable analysis, allowing the system to not simply provide an answer, but to demonstrate how that answer was derived from the available information. The LLM’s ability to interpret and synthesize information is crucial for complex problem-solving and decision-making within the agent architecture, enabling it to move beyond simple data retrieval to nuanced contextual understanding.

The system’s capacity to simulate potential actions relies on the integration of the Language Model with both the Knowledge Base and World Model data. This allows for the generation of hypothetical scenarios stemming from available actions, followed by prediction of resultant outcomes based on established knowledge and modeled environmental states. The system then employs a reasoned evaluation process, assessing each predicted outcome against defined objectives and constraints to determine the action yielding the most favorable result. This evaluation is not based on pre-programmed rules, but rather on the LLM’s ability to synthesize information and extrapolate likely consequences, ultimately enabling selection of an optimal course of action.

The Reasoner Agent’s capacity for nuanced adaptation stems from its utilization of a comprehensive knowledge base, differentiating it from systems reliant on predefined rules. Traditional rule-based systems operate on explicit instructions, limiting their flexibility in novel situations. Conversely, the Reasoner Agent accesses and processes information from a broad knowledge base, allowing it to infer context, identify relevant factors, and generate responses appropriate to the specific situation. This enables the agent to move beyond rigid, predetermined actions and instead formulate adaptive strategies based on the available information, effectively handling ambiguity and complexity that would challenge rule-based approaches.

The Meta-Learner Agent enhances system performance through iterative refinement of operational strategies. This is achieved by continuously analyzing data from past interactions and experiences, identifying recurring patterns in successful and unsuccessful outcomes. The agent then utilizes these patterns to adjust the prompting methodologies and decision-making processes employed by the Reasoner Agent. This adaptive learning process allows the system to improve its ability to predict outcomes and select optimal actions over time, moving beyond pre-defined parameters and enabling increasingly nuanced and effective responses to complex scenarios. The identified patterns are not explicitly programmed but rather emerge from the analysis of empirical data, allowing for generalization to novel situations.

Validation and Scalability: Demonstrating Robust Performance

POLARIS was evaluated utilizing both SWIM and SWITCH, established self-adaptive systems designed to model complex, dynamic environments. SWIM, a wide-area network emulation platform, and SWITCH, a data center network simulator, provided representative workloads for assessing POLARIS’s adaptation capabilities. These exemplar systems were chosen to specifically test POLARIS’s ability to refine adaptation strategies under varying conditions and network complexities. Testing against these benchmarks allowed for a controlled comparison of POLARIS’s performance against existing methodologies in realistically challenging scenarios, demonstrating its potential for improved resource management and network stability.

Evaluation of POLARIS utilizing the Clarknet Trace data set demonstrated a cumulative utility score of 5445.48 when operating within the SWIM environment. This performance metric represents the total accumulated reward or benefit achieved by the system over the duration of the trace. Comparative analysis against baseline systems revealed that POLARIS consistently achieved a higher cumulative utility, indicating improved adaptation strategies and overall performance in this complex, self-adaptive system testbed. The specific methodologies used for calculating utility in SWIM were focused on rewarding efficient resource allocation and successful completion of tasks within the simulated network.

Performance evaluations conducted using the SWITCH system indicate that POLARIS achieves a 27.3% reduction in median response time compared to baseline systems. Concurrent with this improvement in responsiveness, POLARIS also demonstrates a 14.9% decrease in CPU usage under the same operating conditions. These metrics were obtained through direct comparison of POLARIS and baseline implementations on the SWITCH platform, quantifying the system’s efficiency gains in processing demands and resource utilization.

During testing with the SWITCH system, POLARIS demonstrated a significant reduction in disruptive switches – specifically, an 87.1% decrease compared to baseline system performance. This metric quantifies the frequency of transitions between different operational configurations, with fewer disruptive switches indicating improved system stability and reduced operational overhead. The observed reduction suggests that POLARIS effectively minimizes unnecessary reconfiguration, leading to a more consistent and efficient operational state within the SWITCH environment, and ultimately contributing to lowered resource consumption and enhanced reliability.

Towards Truly Autonomous Systems: A Vision for Intelligent Adaptation

The development of POLARIS marks a pivotal advancement in the pursuit of genuinely autonomous systems. Traditionally, systems require constant human intervention for maintenance, performance tuning, and error recovery; POLARIS aims to transcend this limitation through integrated capabilities for self-management, optimization, and healing. This isn’t merely about automation, but about imbuing systems with the capacity to independently assess their own state, identify potential issues, and implement corrective actions – or even proactively adjust to changing conditions. By shifting from reactive maintenance to anticipatory adaptation, POLARIS envisions a future where systems can operate reliably and efficiently with minimal human oversight, representing a substantial stride towards resilient and self-sufficient technological infrastructure.

POLARIS distinguishes itself through a synergistic approach to system autonomy, moving beyond reactive responses to embrace predictive resilience. It doesn’t simply react to problems as they arise, but actively anticipates them by integrating robust reasoning capabilities with continuous machine learning. This allows the system to build an internal model of its operational environment, forecast potential disruptions, and proactively adjust its configuration to mitigate risks. Rather than relying on pre-programmed responses, POLARIS learns from experience, refining its predictive abilities and adaptation strategies over time. This combination of foresight and flexible response enables systems to not only overcome unforeseen challenges, but to maintain optimal performance even amidst substantial uncertainty, representing a crucial step towards genuinely self-managing infrastructure.

The advent of POLARIS signals a potential paradigm shift across diverse technological landscapes. In cloud computing, the system’s self-healing capabilities promise dramatically improved service reliability and reduced operational costs by autonomously addressing performance bottlenecks and security vulnerabilities. Robotics stands to benefit from enhanced adaptability, allowing robots to navigate unpredictable environments and recover from unexpected failures without human intervention. Perhaps most crucially, POLARIS offers a proactive defense mechanism for cybersecurity, moving beyond reactive threat detection to anticipate and neutralize attacks before they can compromise system integrity – a capability that could reshape digital defense strategies and safeguard critical infrastructure. These applications, while distinct, all share a common thread: the promise of more resilient, efficient, and secure systems operating with minimal human oversight.

Ongoing development surrounding POLARIS centers on amplifying its capacity for continuous learning, moving beyond reactive adaptation to predictive system behavior. Researchers are currently investigating advanced reinforcement learning techniques and meta-learning approaches to enable the framework to generalize effectively across previously unseen scenarios. A key focus is extending POLARIS’s capabilities to operate reliably within highly dynamic and unpredictable environments – such as rapidly evolving cybersecurity landscapes or the complexities of real-world robotic navigation – by improving its ability to anticipate and proactively mitigate potential disruptions. This includes exploring methods for transfer learning, allowing the system to leverage knowledge gained in one domain to accelerate learning in another, ultimately paving the way for genuinely resilient and self-improving autonomous systems.

The POLARIS framework, with its emphasis on agentic reasoning and continual learning, embodies a shift towards systems that don’t merely respond to change, but proactively anticipate and refine their behavior. This aligns with a core tenet of robust system design: understanding the interconnectedness of components. As Andrey Kolmogorov stated, “The most important thing in science is not to be afraid of making mistakes.” POLARIS actively embraces a form of controlled experimentation through its multi-agent approach, allowing for iterative refinement of adaptation strategies. The framework acknowledges that complete foresight is impossible; instead, it prioritizes a scalable architecture capable of learning from its own operational experience, thereby reducing the brittleness often found in statically designed systems. This approach implicitly acknowledges that dependencies – in this case, the interplay between agents – are the true cost of freedom in a complex, evolving environment.

Beyond Reaction: Charting a Course for Adaptive Systems

The POLARIS framework, with its emphasis on agentic reasoning, represents a departure from systems merely reacting to perturbation. However, the elegance of this approach lies not simply in enabling adaptation, but in revealing the inherent limitations of current methodologies. A system capable of reasoning about its own adaptation introduces a new order of complexity – the potential for meta-cognitive loops, unintended consequences arising from the agents’ interactions, and the thorny issue of defining ‘improvement’ within a constantly shifting landscape. Modifying one component-the reasoning engine, the reward function, or the agentic architecture-triggers a domino effect, necessitating a holistic understanding of the system’s emergent behavior.

Future work must address the scalability of multi-agentic reasoning. While POLARIS demonstrates promise, extending this approach to systems with hundreds, or even thousands, of interacting agents presents significant challenges. The cost of maintaining coherence, preventing conflicting goals, and ensuring efficient communication cannot be dismissed. Furthermore, the reliance on LLMs, while currently effective, introduces a dependence on models prone to hallucination and susceptible to adversarial manipulation.

Ultimately, the true test of POLARIS, and frameworks like it, will not be their ability to adapt to known unknowns, but to anticipate, and gracefully navigate, the unknown unknowns. The field must move beyond simply building self-adaptive systems, and towards constructing systems capable of learning how to learn, and refining their own adaptive strategies over extended periods. The path forward lies in embracing the inherent messiness of complex systems, and acknowledging that true intelligence resides not in eliminating uncertainty, but in learning to thrive within it.

Original article: https://arxiv.org/pdf/2512.04702.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/