🧐 The second phase of the @TheoriqAI Protocol is where the real "on-field combat" begins. The previous signal agents and observation agents were just there to assist in market watching and data analysis; the ones truly working on-chain are the main characters of this round: the strategy agents and LP agents.

In simple terms, one of these two roles generates ideas, while the other executes them. The strategy agent is responsible for deciding whether to adjust positions, such as assessing the current market volatility, determining if a pool change is necessary, and fine-tuning the strategy details. The LP agent, on the other hand, is the executor; upon receiving the strategy, it directly adjusts liquidity positions on-chain, moving towards higher yields, automatically hedging, and controlling slippage, all of which it accomplishes.

In this stage, they conducted a complete demonstration of the entire process, from front-end data collection to strategy simulation, and then to the actual execution on-chain. The entire Swarm acts like a conscious "liquidity machine," capable of making decisions and running strategies on its own, without the need for manual button pressing.

The difficulty of this system does not lie in automation, but in "dynamic" automation. The market is constantly changing, and strategies must be able to change accordingly. This requires the agent not only to execute but also to judge and adjust itself. What Theoriq aims to do is not just a tool, but a financial intelligent agent that can think for itself, and it feels like they are one step closer to this goal. The key is that it has a leaderboard activity on @KaitoAI.