AI is moving beyond simple text generation. Agents that can use tools, browse the web, and execute complex workflows are the next frontier.
What Makes an Agent?
An LLM (Large Language Model) by itself is just a text prediction engine. An Agent is an LLM combined with:
- Tools: APIs, calculators, or search engines it can call.
- Planning: The ability to break a goal into steps.
- Memory: Remembering past interactions and improved context.
Implementing with LangGraph
While LangChain provided the primitives, LangGraph has emerged as the standard for orchestrating complex agent flows. It allows you to define cyclical graphs where the agent can loop: Thought -> Action -> Observation -> Thought.
The React Pattern
Most modern agents follow the ReAct (Reason + Act) pattern. The model outputs a thought, decides to call a tool, halts execution to wait for the tool output, and then resumes with the new context.
Production Challenges
Building a demo is easy; shipping to production is hard. The biggest hurdles we face are:
- Loop stability: Preventing the agent from getting stuck in infinite loops.
- Cost control: Managing token usage when agents retry tasks.
- Evaluation: How do you test if an agent is "improving"?