What is an Agent?
The news around AI terms can be pretty overwhelming and sound more like a buzzword than something useful. Before we dive deep into how you can build agents in RailTracks, let's first understand what an agent is.
An agent is a self-contained unit that can perform a specific task or set of tasks autonomously. It has the ability to make decisions, take actions, and act within its environment to achieve its goals.
The key abilities of the agent include:
- Autonomy: Agents can operate independently within the boundaries you define
- Adaptability: Agents can adjust their behavior based on the environment and the tasks at hand
- Goal-Oriented: Agents are designed to achieve specific objectives or complete tasks
- Interaction: Agents can communicate with other agents or systems to gather information or perform actions
- Stateful: Agents maintain context and history and use it to inform their decisions
LLMs as Agents
Reinforcement Learning and other AI techniques have trained specific agents to operate in their environments for a while now, but LLMs have changed the game and made it much easier to use their generalized intelligence to accomplish complex tasks and goals. This ability makes them uniquely suited to operate as the brain for your agentic system.
graph LR
User[User] --> LLM[LLM Agent]
LLM --> Tools[Tools]
Tools --> Environment[Environment]
Environment --> Tools
Tools --> LLM
LLM --> User
style LLM fill:#e1f5fe
style User fill:#f3e5f5
style Tools fill:#fff3e0
style Environment fill:#e8f5e8
Real World Applications
Agents are already being used in real world applications such as:
- Vibe Coding Tools (Cursor, Windsurf, etc.)
- NPC Interactions in Games (AI Village)
- Technical Documentation Writing (ParagraphAI)
- Deep Research Tools (GPT Deep Research)
Related Topics
Build Your Own
We have build RailTracks with developers in mind; with just a simple prompt and a bit of Python, you’re already well on your way to building your first agent. Get started Building with RailTracks