Building a Simple AI Agent Workflow Without Frameworks
The author shares their experience of building a useful AI agent workflow without relying on complex frameworks. They emphasize that an AI agent is just a loop, and the hard part is defining what the agent should do, not the code implementation.
Why it matters
This article provides a pragmatic perspective on building useful AI agents without getting bogged down in complex frameworks, which can be helpful for AI developers and enthusiasts.
Key Points
- 1An AI agent is just a loop: input -> LLM thinks -> calls tool or returns result
- 2Frameworks just add structure around this basic loop, but the core logic is simple
- 3The author built a 3-step agent to research a topic, summarize content, and produce a briefing
- 4The only setup needed is a language model and a few custom tools, no complex infrastructure
Details
The author explains that they initially got bogged down trying to learn various AI frameworks like LangChain, AutoGen, etc. But they realized that an AI agent is fundamentally just a loop - it receives input, processes it using a language model, and either calls an external tool or returns a result. The frameworks are just wrappers around this basic structure with different opinions on how to organize the code. The author then shares the architecture of a simple 3-step agent they built to research a topic, fetch and summarize relevant content, and synthesize a structured briefing document. This agent only required a language model (in this case, Anthropic's Claude) and a few custom tools, without needing complex features like memory, state management, or multi-agent orchestration that many frameworks provide. The author argues that this simple linear chain of tool calls is sufficient for 90% of real-world use cases.
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