Agentic workflows involve self-directing agents that follow a general path in a hybrid approach, though they can become inefficient due to latency and high compute usage. Performance problems arise in overly complex workflows. Key optimization strategies involve minimizing steps, merging related tasks, and reducing unnecessary decisions. Starting with the simplest form—potentially a single agent—is advisable before expanding to more complex workflows. Each additional step or model call can introduce delays and increase the risk of errors, necessitating careful design to achieve optimal efficiency and flexibility.
The first time I built an agentic workflow, it was like watching magic, i.e., until it took 38 seconds to answer a simple customer query and cost me $1.12 per request.
Some of these pain points include slow execution, high compute usage, and a mess of moving parts.
Every model call adds latency. Every extra hop is another chance for a timeout. And let's not forget about how it also augments our chance of hallucinations.
When I design a workflow, I always start with a single agent (because maybe we don't need a workflow at all).
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