FIT3226 Agentic and distributed AI: Multi-agent systems, swarms and artificial life

When you finish this unit, you should be able to:

1. 1

   Configure computational environments, frameworks and simulation tools to build and evaluate agentic and multi‑agent AI systems operating in dynamic conditions;

2. 2

   Apply systems-thinking methods to analyse and compare agentic AI, coordinated multi-agent systems, and emergence/artificial-life approaches, explaining how local rules and interactions produce global behaviours in complex adaptive systems;

3. 3

   Design system architectures, coordination protocols and rule‑based interaction models that balance decentralisation, adaptability and control in distributed AI systems;

4. 4

   Implement interacting agents that can plan, reason, and execute multi-step actions (including tool use where appropriate), integrating coordination logic and handling partial information or dynamic environments;

5. 5

   Engineer, run, and interpret evaluations of multi-agent behaviour (e.g., simulations, ablations, and performance/emergence metrics) to iteratively improve robustness, coordination quality, and goal attainment.