2. AI Agents: The Core Concept
In the simplest terms, an agent is an application that observes the environment, reasons about it, and acts to achieve a goal—often using external tools. While advanced generative models can produce sophisticated outputs, they do not inherently plan or execute real-world tasks. Agents fill this gap.
An agent generally consists of:
The Model – A large language model (LLM) or specialized AI that processes user queries, reasons about data, and proposes solutions.
Tools – APIs or modules that let the agent go beyond static knowledge, e.g. for real-time data retrieval or financial transactions.
Orchestration – The layer that stitches everything together, defining how the agent cycles through observation, reasoning, and action until it meets its objectives.
2.1 Agents vs. Standalone Models
A standalone model can generate a single answer (e.g., “The market might go up”); however, it can’t keep updating that prediction automatically or place actual trades.
An agent can store session history, adapt to new data, and use tools to perform actions such as “Open a short position on Polymarket” or “Mint a new derivative token.”
By bridging these elements, agents can become dynamic market participants rather than passive oracles.
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