AI agents are talked about without enough context. In reality, there’s a strict hierarchy of complexity. 👇
AI agents are simply computer programs designed to mimic human intelligence. They perceive your environment, make independent decision, and take actions to achieve specific goals. AI agents can run autonomously anywhere, in customer service, decentralized finance, launch pads, etc.
There are 5 core architectural types of agents in the AI ecosystem. The agents are not built to run equally, ranging from simple reactive loops to complex learning systems.
1️⃣ Simple Agents: These agents are also called reflex agents. They operate entirely on programs like “if this, do that”. They don’t necessarily look at past events or plan for the future. For example, a Simple Reflex agent deployed in the thermoregulatory system of a room would like “if room temperature drops below 18°C, turn on heater”.
2️⃣ Model-Based Reflex Agents: unlike reflex agents, they keep track of things that may not be seen right now by building an internal map of their environment. They function by a combined history of past perceptions and current inputs to maintain a picture of reality. They understand cause and effect, and how their action affects their environment. Something like “if I (the agent) move forward into this intersection, I will block the car behind me”. For example, a self driving car, or a food delivery robot.
3️⃣ Goal-Based Agents: They combine their environmental data and their end goal to figure out a path to get there. For example, Google Maps AI agents re-routing you. It knows where you want to go (goal), evaluate the paths ahead (environmental data) , and chooses the turns to get you to your specific destinations.
4️⃣ Utility-Based Agents: These agents don’t care about merely reaching the goal, they care about how well they achieve the goal. Still using Google Maps as an example, unlike goal-based agents that finds sequence of actions (turns) that get you to your destination, Utility-Based agents evaluates the paths, and calculate the one that will give you the highest satisfaction (lowest time, lowest cost, and smoothest ride).
5️⃣ Learning Agents: They are the pinnacle of AI agents. They start with a baseline but actually learn from their mistakes. They observe the real world, figure out what went wrong or right, and actively update their own code and strategies over time. For example, models in your Netflix account notices when you keep skipping sci-fi for comedy. It rewrites its internal rules, and fills your home screen with comedy next time.
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.@aave V4 is growing day by day - but there might be a part of DeFi users who haven't yet taken the time to understand the difference between V3 and V4.
That's the exact topic I'm covering in this video.
You can also try out the Aave V4 dashboard on @DeFiSaver - and watch more DeFi educational content on the Pick DeFi Youtube channel.