லாங்செயின்: உங்கள் ஏஜெண்டு கட்டமைப்பின் மீது முழுமையான கட்டுப்பாட்டிற்கான ஒரு கட்டமைப்பு

This episode of Exploring Modern AI in Tamil podcast explains LangChain core concepts simply for someone new to building AI agents.

- Focuses on how messages and models work together to create an agent.

- Explains how models make decisions to call external tools for the agent.

- Explains why production agents need tools like observability and human in the loop.

- Explains how streaming improves user experience for long-running agent responses.

- How should developers choose the right model provider for their specific agent?

- What strategies ensure agents remain reliable during complex multi-step reasoning tasks?

- How do multimodal inputs change how messages are structured within LangChain.

- Summarizes why modularity helps developers switch model providers without rewriting code.

- Details how developers can control reasoning effort levels and budgets in advanced models.

- Describes how Pydantic or JSON schema ensures models return consistent structured data.

- Outlines simple design patterns for managing conversation history and agent memory.

- Discusses optimizing agent performance using batch processing and parallel tool calls.

- Describes how to use LangSmith for debugging and monitoring agent performance in production.

- Explains how developers can use model profiles to dynamically manage agent capabilities.

- How can developers use RunnableConfig to manage production metadata and execution control?

- Explains how to configure models for multi-step reasoning and token budget management.

- Outlines best practices for handling rate limits and API retries in production.

- Discusses the pros and cons of running models locally versus using cloud APIs.

- Compares the flexibility of LangChain versus the built-in features of Deep Agents.

- Contrasts when a developer should choose pure LangChain versus the Deep Agents framework.