Explainer

A great article to capture the principles we use at adaptive-emergent. Lessons forgotten in the cloud. https://www.tredence.com/blog/agentic-swarms-lessons-from-nature-for-machine-intelligence

The knapsack problem is the classic optimisation problem: You have a backpack that can carry only a certain weight. You have a set of items, each with a weight and a value.

Over this 7-part series, we’ve explored how natural systems — ants, bees, birds, and brains — solve problems through emergence, not instruction.

In particular areas of human endeavour, precision in language matters, such as contract law. Sometimes the language is numbers, like in accounting or science.

This is the fifth of seven articles in the series comparing Nature with AI and how they approach agents. Part four is accessed here, the full series from here.

In previous articles we defined terms like agent and swarms, and looked at a major difference between natural and artificial adaptation, time.

“Nature does not hurry, yet everything is accomplished.” – Lao Tzu In part one and part two, we’ve explored how biological agents — ants, birds, bees — operate without central control, memory, or explicit goals.

In part one, we asked what it means to call something an agent. We contrasted modern AI agents — designed to achieve explicit goals — with biological agents like bees or ants, which follow simple rules without necessarily “understanding” their purpose.

In 2025 the term “agent” has been dusted off and it is seeing another time in the sun. My first experience was with printer drivers… remember printers?