DAGWorks’s Substack

Many AI applications start off simple – you query some hosted model, do a little bit of prompt engineering, process/store/index the result, and stream it back to the user. You model this as a simple ping-pong application – (AI → Human → AI, etc…). Inevitably, as you hit some success, you will need more from your application. The natural next step is to enable it to call tools, or dive into a more complicated research question (E.G. with chain of thought prompting). This is when it turns into an agent – an AI system that dynamically directs their own processes and tool-usage, either with or without human input. An agent is often modeled by a graph with state – it has a set of capabilities and needs to dynamically decide the order.

As your agent’s capabilities grow in complexity, you likely start to realize that it needs to execute multiple tasks at once – generate several candidates for user/LLM selection, leverage various tools simultaneously, etc… While you will still model this as a graph, you end up being in multiple states at once, and have to worry about distributing the computation and coordinating state between tasks.

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