Agentic AI
What Is Agentic AI?
Traditional artificial intelligence is typically reactive; it answers questions, generates text, or classifies data based on specific prompts. Agentic AI represents a shift toward proactive intelligence. These systems are able to break down complex, high-level intents into a series of smaller, actionable tasks.
Unlike simpler AI agents, which require a human to guide every step, an agentic system can reason through obstacles and interact with external digital environments to achieve a final objective. That being said, they still require a human to set goals and objectives, whereas fully autonomous agents can self-initiate operations.
How Does Agentic AI Work?
The functionality of Agentic AI is built on a continuous loop of perception, reasoning, and action. These systems utilize Large Language Models (LLMs) as a central reasoning engine to create a plan of action. They are also supported by memory modules that allow the agent to learn from past interactions, and tools that enable it to interact with the world.
In a blockchain environment, an agent can use an oracle to perceive real-world data, such as price fluctuations or network congestion. It then uses its reasoning framework to determine if a specific action, such as a token swap, aligns with the user’s original goal. Finally, it executes the action by interacting with smart contracts. This enables autonomous economic activity where agents pay for compute, data, and services onchain without constant human intervention.
Security and Autonomous Agency
When agentic AI systems transition from processing information to moving value, they introduce new risks such as intent drift, where an agent’s reasoning leads it to take actions that deviate from the user’s original goals.
As such, maintaining a clear boundary between autonomous action and human authority is essential, especially when these systems require the ability to carry out transactions. Signers (hardware wallets) can act as a digital firewall in these instances, requiring manual approval for high-value actions while letting agents handle routine optimization.
