Here’s a brief discussion on investment thoughts for different categories of AI Agents:

1) Standalone AI: Strong user perception, focused application scenarios, short validation cycles, but limited growth potential. It's essential to experience it personally before investing; for instance, some newly released strategy analysis standalone AIs, hearing about them is not as good as trying them out yourself. For example: $AIXBT, $LUNA.

2) Framework and Standards: High technical difficulty, grand goals and visions, key acceptance by developers, and a high ceiling. When investing, consider the project's technical quality, founder background, narrative logic, and actual implementation. For example: $arc, $REI, $swarms, $GAME.

3) Launchpad Platforms: Well-developed tokenomics, strong ecosystem effects, and the potential to create positive flywheel effects. However, if there are no hits for a while, market expectations will significantly decline. Therefore, it is recommended to enter the market when enthusiasm is high and innovation is frequent; during market lulls, it’s better to observe. For example: #Virtual, $MetaV.

4) DeFi Trading AI Agent: AI Agents have already been applied to the ultimate form of the crypto market, with a vast imagination space but many uncertainties, such as the accuracy of matching and execution. So it's best to experience it first, confirm the effect, and then decide whether to invest. For example: $BUZZ, $POLY, $GRIFT, $NEUR.

5) Creative Specialty AI Agent: This mainly depends on whether the creativity is sustainable. Strong user stickiness and IP value, but early hype may affect later market expectations. Tests the team's ability for continuous innovation. For example: $SPORE, $ZAILGO.

6) Narrative-Driven AI Agent: Attention needs to be paid to whether the project team is reliable, can continuously update, whether the white paper's plans are gradually implemented, and most importantly, whether it can maintain market dominance within a narrative cycle. For example: #ai16z, $Focai.

7) Business Organization-Driven AI Agent: Tests the resource coverage of the B-end market, advancement of product strategies, and continuously updated milestones. Platform data is also very critical. For example: #ZEREBRO, #GRIFFAIN, $SNAI, $fxn.

8) AI Metaverse Series AI Agent Platforms: The application of AI Agents in 3D modeling and metaverse scenarios does have advantages, but due to high commercial vision, significant hardware dependencies, and long product cycles, special attention should be paid to the project's continuous iteration and the manifestation of practical value. For example: $HYPER, $AVA.

9) AI Platform Series: Whether it's data, algorithms, computing power, or inference fine-tuning, DePIN, etc., they all target the consumer market. AI Agents represent a market with huge potential; the key is how to access the AI Agent ecosystem. For example: @hyperbolic_labs, @weRoamxyz, @din_lol_, @nillionnetwork.

Note: These are just part of the classifications for AI Agents; the mentioned targets are for learning reference only and not investment advice. DYOR!