Over this 7-part series, we’ve explored how natural systems — ants, bees, birds, and brains — solve problems through emergence, not instruction.
We’ve contrasted that with modern AI agents: fast, centralised, memory-bound, and engineered to optimise.
- Part 1 asked what it really means to call something an agent, and laid the foundation for contrasting biological and artificial designs.
- Part 2 introduced swarms that don’t think — systems where intelligence emerges from local rules, not planning.
- Part 3 dug into evolution vs instant learning, showing how nature’s slow adaptation creates robustness, while AI races ahead with fragile speed.
- Part 4 examined purpose vs emergence: who defines what an agent should do: a designer, or the system itself?
- Part 5 explored collective memory, where nature stores information in trails, dances, and distributed state rather than internal representations.
- Part 6 challenged the metaphor of AI “swarms”, showing how most are coordinated scripts, not emergent systems.
Simplicity is a Feature
There’s power in simple rules. Flocks, hives, slime moulds — none of them need deep models or layered abstractions. But collectively, they solve problems, allocate resources, and adapt to changing conditions.
Instead of designing ever more complex agents, perhaps we need to design simpler ones that interact more richly.
Applying Natural Principles: What This Could Look Like in Practice
- Agents with instincts instead of goals. This is the “three simple rules” programming where the result emerges. At Redgrid, we told an energy controller in a house to react to local events; the result was many houses in a suburb averted a blackout on a hot day.
- Memory encoded in the environment rather than central stores. Ants leave clues of what they find, which are reinforced by those who follow, similar to strengthening synapses. Memories fade through atrophy.
- Evolutionary biology through natural selection is useful to remember when building systems today. Instead of chasing AGI and getting involved in the AI arms race, the system can be assured to be stable over a few generations.
- Feedback loops instead of reward maximisation. A biological being continuously senses, reacts, and adjusts to its environment in real time, rather than attempting to reach a goal. Success is by “staying in the game”. I wonder if this is anti-capitalist? Probably.
- Failure-tolerant architecture, where errors create opportunity, not collapse.
This doesn’t mean abandoning machine learning. It means asking harder questions about how systems adapt, evolve, and persist — not just how they perform.
“A finite game is played for the purpose of winning, an infinite game for the purpose of continuing the play.”— James P. Carse, Finite and Infinite Games (1986)
A closing thought: Perhaps the future of AI isn’t to build better winners, but better players.
What’s Next for adaptive-emergent
In 2025, we have accumulated a number of working models that demonstrate these principles, but we have some housekeeping to do first:
- This series was originally published in LinkedIn, but it is on the move to a shiny new blog on the adaptive-emergent website and a few other places ;)
- There is much to add to the blog, in the area of biomimicry and the work of Janine Benyus. Since we are interested more in communication networks, we are looking at rhizomes (ginger roots) and mycelium (underground fungal networks).
- An ongoing book review section that looks at Emergence though Chaos Theory to cellular automata and synthetic biology.
- A case study of Toronto-based Encycle, who use distributed devices to efficiently improve HVAC management in large buildings.
- We are rapidly becoming fanbois of Nobel Prize winning Demis Hassabis, co-founder and CEO of DeepMind. Don’t miss the award-winning biography of him “The Thinking Game”, and we need to share a podcast very soon. DeepMind’s latest AlphaEvolve is some end-game stuff.
We hope you enjoyed the series, feel free to like and share if you did. There are more to come.
