Disruptions, whether minor perturbations or significant, are integral to the evolution of complex systems. They challenge the flow of the system, expose vulnerabilities, and compel systems to adapt. The adaptations that emerge from these disruptions can lead to divergent outcomes: some lead to failure, while others enhance system performance and resilience.

When adaptations lead to failure, it is often due to a system's inability to cope with the magnitude or nature of the disruption. Structural collapse can occur when foundational elements are compromised, resulting in a breakdown of essential functions. For example, an economic system may collapse under the weight of a financial crisis if its regulatory frameworks are inadequate to manage extreme volatility. Maladaptation is another pathway to failure, where responses to disruptions are misaligned with system flow and performance. A system might overcompensate for a past disturbance, allocating resources inefficiently and neglecting emerging challenges. Additionally, a loss of diversity within a system can reduce its adaptive capacity. Homogenization limits the range of responses available, making the system more susceptible to future disturbances that require novel solutions.

Conversely, adaptations that lead to improved performance or resilience often involve embracing complexity and fostering diversity. Systems that incorporate redundancy—multiple pathways to achieve the same function—can absorb shocks more effectively. In ecological systems, for example, biodiversity acts as a buffer against environmental changes, as different species can fulfill similar roles within the ecosystem. Feedback loops play a crucial role in facilitating adaptive responses. By monitoring outcomes and adjusting behaviors accordingly, systems can learn from disruptions and refine their strategies. Innovation frequently arises in the wake of disruptions, as challenges foster creative solutions. The development of new technologies or processes in response to a crisis can propel a system to higher levels of performance and resilience (Figure 2).

Despite significant advancements in modeling and analytical tools, our comprehension of nonlinear complexity in dynamic systems remains limited. Nonlinearity implies that cause and effect are not proportionally related; small changes can lead to significant impacts, and large inputs may produce negligible effects. This unpredictability complicates our efforts to predict system behavior and implement effective and early interventions.

One of the primary challenges is the presence of unforeseen consequences. Complex systems are characterized by co-dependencies that are not always apparent. An action intended to rectify an issue in one part of the system may trigger adverse effects elsewhere. For instance, implementing a policy to stimulate economic growth could lead to deficits if ecological impacts are not considered, therefore the need for system-level understanding.

Scaling issues further complicate the picture. Behaviors observed at one scale may not manifest similarly at another. A strategy effective at the micro-level may fail when applied broadly, as interactions and feedbacks differ across scales. This makes it difficult to generalize findings and develop universal solutions.

Data limitations are another significant obstacle. Capturing the myriad variables and interactions within a complex system is often difficult. Incomplete data can lead to models that fail to accurately represent the system's dynamics, resulting in misguided decisions. Even with extensive data, the sheer volume and complexity can overwhelm analytical capacities, necessitating new approaches to data processing and interpretation.

To address these challenges, a systems engineering approach tailored to composite AI offers a promising pathway. Unlike traditional AI, which may focus operate within defined parameters, composite AI integrates multiple AI systems, each contributing unique capabilities. This approach can mirror the modularity and relationships of complex systems, enabling AI to better understand and interact with them.

By learning from failure, composite AI systems can understand and improve resilient strategies over time. Analyzing the outcomes of disruptions allows AI to identify vulnerabilities and strategies accordingly. This learning process involves not just reacting to failures but proactively seeking to understand underlying causes and potential future outcomes.

Understanding and identifying resilience factors is another critical aspect. Composite AI can process vast amounts of data to detect patterns and features that contribute to a system's robustness. By recognizing these factors, AI can help reinforce them, enhancing the overall resilience of the system.

Modulating resilience involves adjusting system parameters in real-time to respond to changing conditions. Composite AI can facilitate this by continuously monitoring system performance and environmental factors, making dynamic adjustments to maintain optimal functionality. This capability is essential in environments where conditions can shift rapidly and unpredictably.

Steering systems toward beneficial outcomes requires a forward-looking perspective. Composite AI can employ predictive analytics to anticipate potential disruptions and their impacts. By integrating long-term goals into its operational framework, AI can make decisions that align with desired future states, balancing immediate needs with strategic objectives.

The implementation of a systems engineering approach for composite AI has far-reaching benefits across various domains. Enhanced system performance is a direct outcome, as adaptive AI can optimize operations in real-time, improving efficiency and effectiveness. In industries such as manufacturing or logistics, this can lead to significant productivity gains and cost reductions.

Reduced costs are also realized through proactive adaptation. By anticipating and mitigating disruptions before they escalate, organizations can avoid the expenses associated with reactive measures. Maintenance can become predictive rather than reactive, minimizing downtime and extending the lifespan of assets.

Sustainability is another critical benefit. Resilient systems are better equipped to handle environmental and economic changes, contributing to long-term viability. The human impact of resilient complex systems is profound. As these systems underpin essential services and infrastructure, their robustness directly affects quality of life. In healthcare, resilient systems can improve patient outcomes by adapting to emerging health threats. In urban planning, they can enhance livability by optimizing resource use. By leveraging AI's capacity to process complex data and adapt rapidly, we can develop solutions that were previously unattainable, driving innovation and progress across multiple sectors.

Biological systems offer a rich source of inspiration for developing resilient, adaptive technologies. Nature has evolved mechanisms over millennia that enable organisms to survive and thrive in the face of constant change and disruption. These mechanisms provide valuable insights into how we might design artificial systems with similar capabilities.

Systematic adaptation is a hallmark of biological resilience. Organisms continually adjust to their environments through processes such as homeostasis and acclimatization. These adjustments occur at multiple levels, from cellular responses to changes in gene expression. By mimicking key adaptive processes, composite AI systems can develop the ability to adapt at different levels of operation, enhancing overall resilience.

Genetic resilience, for example, through mutation and natural selection is another key concept. DNA mutations introduce variability, and while many mutations are neutral or harmful, some confer advantages that improve an organism's fitness. Over generations, advantageous traits become more prevalent, enhancing the species' resilience. This evolutionary process can inspire algorithms that incorporate variation and selection mechanisms, allowing composite AI systems to evolve and improve over time specific part of their components.

Robustness and flexibility in biological systems are achieved through redundancy and diversification. Multiple pathways can achieve the same function, and organisms often possess backup systems that can compensate when primary systems fail. This redundancy ensures that essential functions are maintained despite damage or disruption. Applying this principle to AI involves creating systems with overlapping and modular capabilities, ensuring continuity when components fail.

Information encoding in biological systems is efficient and effective. DNA serves as a compact repository of information, encoding not only the blueprint for building an organism but also the adaptations that have proven successful over generations. Similarly, AI systems can benefit from encoding knowledge and successful strategies in ways that can be easily accessed and applied, facilitating learning and adaptation. Even though this is still in early stages, investing on similar encoding architectures can help these endeavours.

By drawing on these biological principles, we can develop computing adaptive intelligence that is not only robust but also capable of evolving in response to new challenges. This approach represents a significant shift from traditional engineering methods, embracing complexity and change rather than seeking to eliminate them.

Resilience in dynamic complex systems is a multifaceted and pressing challenge. The nonlinear complexity inherent in these systems defies simple solutions, requiring innovative approaches that can navigate unpredictability and interdependence. By learning from disruptions and embracing the lessons offered by biological systems, we can develop AI technologies that not only withstand disturbances but also leverage them for growth and improvement,

Implementing a systems engineering approach for composite AI is a critical step toward this goal. By enabling AI to learn from failure, understand resilience factors, modulate adaptability, and steer systems toward beneficial outcomes, we position ourselves to harness the full potential of complex systems. This has profound implications for the future of humanity, as resilient systems are essential for addressing global challenges such as climate change, resource scarcity, and social instability.

The benefits of this approach are extensive, spanning enhanced performance, cost reduction, sustainability, and human well-being. As we integrate these resilient AI systems into the fabric of our societies, we can create a foundation for sustainable progress and innovation. The convergence of technology and biology offers a transformative path forward, one that recognizes the value of adaptability, diversity, and evolution in building robust systems.

In embracing these principles, we not only improve our ability to manage complex systems but also align ourselves more closely with the natural processes that have sustained life on Earth for billions of years. This synergy between technology and biology holds the promise of a future where humanity can thrive in harmony with the dynamic complexities of our world.