How AI Agents Actually Work: From A Different Lens

At their core, AI agents mimic human cognitive processes but in a highly efficient, automated way. They analyze data, plan actions, execute tasks, and learn from interactions—all within a continuous loop. Whether they are optimizing a DeFi portfolio, managing blockchain operations, or automating workflows, their structured approach allows them to function independently while adapting to new challenges.

1. LLM (The Brain)

• The large language model (LLM) serves as the agent’s reasoning engine.

• It "thinks," processes information, and makes decisions based on inputs.

• Essentially, it acts as the mind of the agent, enabling understanding and problem-solving.

2. Planning (The Decision-Maker)

• Planning determines what needs to be done to achieve a goal.

• It identifies required information, selects the right tools, and evaluates when the task is complete.

• This structured thought process transforms goals into actionable steps.

3. Memory (Awareness)

• Memory gives the agent context and adaptability over time.

• It can be:

  • Short-term: Remembering the current conversation or task.

  • Long-term: Retaining valuable data for future decisions, such as market trends or past outcomes.

4. Tools (The Hands)

• Agents don’t just think—they act.

• Tools are the APIs and functions an agent uses to complete tasks, such as:

  • Querying blockchain data.

  • Executing trades.

  • Communicating with other agents.

5. While Loop (The Grind)

• The agent continuously cycles through reasoning, planning, and acting until the goal is met.

• This iterative loop ensures the agent refines its actions until it achieves success.

Example: Portfolio Balancer Agent

Imagine an AI agent designed to optimize portfolio allocation:

• LLM (Brain): Analyzes the market and suggests optimal allocation percentages.

• Planning: Considers user preferences, market risks, and reallocation strategies.

• Memory: Stores portfolio history, past trends, and risk parameters for future decisions.

• Tools: Uses APIs to fetch price data, execute rebalancing actions, and interact with staking platforms.

• While Loop: Continuously checks if the portfolio meets risk-reward criteria. If not, it reallocates; if balanced, it stops.

Final Thoughts

AI agents are structured systems that blend intelligence, planning, and action to operate autonomously. Whether optimizing portfolios or executing blockchain tasks, they rely on reasoning, memory, tools, and iterative decision-making to function effectively.