The text discusses the challenges of building reliable agentic systems, which consist of both workflows and agents. The author highlights that agent abstractions can make it easy to get started, but often obfuscate the complexity of making sure the LLM has the appropriate context at each step. The author proposes LangGraph as a solution, an event-driven framework for building agentic systems with a declarative syntax and built-in support for streaming, fault tolerance, and human-in-the-loop patterns. The author also discusses the importance of understanding the value of a framework, common questions about agent frameworks, and how to compare different frameworks. They argue that most agentic systems are a combination of workflows and agents and that using a simple tool-calling loop may not be sufficient for all use cases, but can be beneficial when trained on specific tasks with large amounts of data. The author also critiques OpenAI's take on agent frameworks, arguing that it conflates different dimensions and provides misleading information.