CAS behavior is driven by the inter-relationships, inter-action, and inter-connectivity of the agents within a system and between a system and its environment. The relationships and interactions between the agents are generally more important than the agents themselves, for example, in a human body, the brain operates quite independently and so does the digestive system. However, the interaction between these subsystems, which are agents themselves, is critical for the optimal functioning of the larger system. The digestive and respiratory system may seem disconnected, but digestion cannot happen unless the respiratory system provides it with oxygen, and the respiratory system cannot function unless the digestive system converts food into energy. Some systems are based entirely on interactions between agents, for example, an economy cannot function unless there are both buyers and sellers for goods and services.

The driver behind agents interacting with the environment and with other agents is always some purpose. For example, the primary purpose behind all living species interacting with the environment is survival. While this interaction is necessary and unavoidable, it may not be so for other purposes, for example, people trade in the stock market to create wealth and people join a social organization like the Rotary Club for multiple purposes, like social service and fellowship. Even people getting together to celebrate a birthday or a wedding is a purpose-driven behavior.

The agents in a CAS are loosely coupled. This implies that when some agents are removed or when a part of the system fails, the rest of the system is either not impacted or recovers quickly, for example, if some investors leave the stock market, the market continues to function normally. When a large financial institution like the Lehman Brothers collapses, it might bring down a few other institutions with it and make the economy wobble a bit. However, the economy will eventually recover.

The key to being loosely coupled is flexibility in the structure of the system and the diversity of behavior of the agents. If the agents behave in a coordinated or unidirectional manner, the system's behavior will change to being tightly coupled. A stock market crash leading to panic selling and a run on a bank are examples of tightly coupled behaviors.

The more variety there is in a CAS, the stronger it is. The diversity in a CAS leads to ambiguity and paradox. However, a CAS uses contradictions and uncertainty to create new possibilities to evolve with and adapt to the environment. This reinforces the idea of bounded instability or the edge of chaos that is characterized by a state of paradox: stability and instability, competition and cooperation, order and disorder.

Democracy and financial markets are examples of a CAS where a variety of agents leads to the strength of these systems. In living systems, the importance of genetic diversity has also been widely recognized.

According to The National Gardening Association:

Complexity in a CAS refers to the potential for emergent behavior in complex and unpredictable phenomena. There is constant action and reaction to what other agents are doing. From the interaction of the individual agents arises some kind of global property or pattern, which is something that could not have been predicted from understanding each particular agent, for example, the overall behavior observed in the economy is a result of the countless decisions made by millions of individual people. Any coherent behavior in a system arises from competition and cooperation among the agents themselves.

A poignant example is that if we were to take all the food shops in a town and divide all the food by the number of people living there, we would find a pattern that there is always one-to-two weeks' worth of food supply in the town. However, this is achieved without a food plan for the town or a formal controlling process.

Another example is of a termite hill that has an amazing architecture, with a maze of interconnecting passages, large caverns, ventilation tunnels, and much more. Yet there is no grand plan, the hill just emerges as a result of the termites following a few simple rules.

In a CAS, the relationship between cause and effect is not necessarily linear, and sometimes not even correlated. Small changes can have a surprisingly profound impact on overall behavior, or vice versa, a huge upset to the system may not affect it. An example of a nonlinear relationship is how Bearings Bank was brought to closure by the actions of just one person, Nick Leeson. The fluttering of butterflies in Brazil causing tornadoes in the state of Texas in the USA [xiii] is an example of a lack of direct correlation between cause and effect. Hence, the causes of many effects may be found only in hindsight, which then may lead to interpreting them over a period of time as patterns.

There is a fine line between order and chaos. A system in equilibrium does not have the internal dynamics to enable it to respond to its environment and it will slowly (or quickly) die. Too much order implies too many constraints and that stifles innovation and creativity. An automobile is an example of an orderly system, which (usually) behaves in a very predictable manner. A system in chaos ceases to function as a system, until order is restored, for example, a severe traffic jam due to a failed traffic signal at a busy intersection. Hence, the most productive state to be in is at the edge between order and chaos, where there is maximum variety and creativity, leading to new possibilities. CASs function best when they combine order and chaos in an appropriate measure, for example, there are some unwritten rules about traffic in Johannesburg, South Africa [xiv], which have emerged from the city being on the edge of order and chaos.

The key to understanding the word chaos in this context is to understand it not as anarchy, but as a lack of structure. A CAS is ruled by the second law of thermodynamics: it is in a constant state of equilibrium, entropy, or disorder, which will keep increasing such that the system will wind down and eventually die, unless it renews itself.

In a CAS, the rules governing the functioning of the system are quite simple. A classic example is that all the water systems in the world (all the streams, rivers, lakes, oceans, waterfalls, and many more, with their infinite beauty, power, and variety) are governed by the simple principle that water finds its own level. The simplicity in rules enables self-organization, which is the key to the system being on the edge of order and chaos, and effectiveness over efficiency.

A CAS, once it has reached the state of "being good enough," that is, the energy wasted is less than the energy spent on improving itself, will trade off increased efficiency every time in favor of greater effectiveness. A simple example is the human body, which will start burning stored fat in the absence of food.

The collective behavior of the agents leads to the formation of broad patterns, which are far more predictable than the behaviors of an individual or a group of agents. The economy has patterns of recession-recovery-boom-slowdown. The weather has a pattern of seasons. While the patterns are largely predictable, the timing of the onset of a pattern is much less predictable. When the economy is in recession, when the recession will end and when recovery will start cannot be predicted.