Building an AI agent isn’t magic- it’s a thoughtful step-by-step process.
It all starts with the system prompt, where you define the agent’s goals, role, and instructions—like giving your teammate clarity on what they’re supposed to do. Next comes the LLM (Large Language Model), the brain of your agent.
Think of it as choosing the right foundation: the model and its parameters that shape how the agent reasons.
Once the brain is ready, you empower it with tools. These can be simple functions, APIs, or even other agents—basically, the “hands” that let it take action in the real world. But a good agent also needs memory.
Just like us, it needs both short-term and long-term memory: working memory for immediate context, vector databases for semantic recall, and even SQL or file storage for structured data.
Now comes the magic of orchestration—this is where workflows, triggers, and agent-to-agent communication bring everything together. To make it usable, you add a UI so humans can interact seamlessly.
And finally, AI evaluation keeps the agent sharp by analyzing, measuring, and improving its performance.
In short, building an AI agent is about combining brains, tools, memory, and orchestration into a system that feels almost human in how it thinks and acts.
'IT Technologies > 기계학습 Machine Learning' 카테고리의 다른 글
| Microsoft, LinkedIn, and ... released a professional certificate in Generative Al (0) | 2023.11.13 |
|---|---|
| 머신러닝 라이브러리, 텐서플로우의 이해 (0) | 2018.06.30 |
| 딥러닝 초보 강좌 Deep Learning (0) | 2017.11.01 |
| Sony Neural Network Console (0) | 2017.10.29 |
| Business applications of Machine Learning (0) | 2017.10.19 |
