Building an internal agent: Code-driven vs. LLM-driven workflows

4 months ago

The article discusses the comparison between code-driven and LLM-driven workflows for building internal agents.
Initially, the author believed LLM plus tool-usage could solve complex workflows but later realized some problems are simpler with software.
A real-world example is given where an LLM-powered workflow was used to mark merged pull requests in Slack but sometimes incorrectly, causing inefficiency.
The solution involved implementing support for both LLM and code-driven workflows, allowing custom Python scripts to handle cases where LLMs aren't reliable.
The hybrid approach uses LLMs for initial workflow handling and switches to code-driven workflows as a progressive enhancement when needed.
The author concludes that even as models improve, using them narrowly for cases requiring intelligence rather than iterative workflows is a long-term strategy.

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