I’ve been working on building an agentic AI workflow system for business use cases and one thing became very clear very quickly. This is not about picking the right LLM.
The real complexity starts when you try to chain reasoning, memory, and tool execution across multiple steps. A single agent works fine for demos. The moment you introduce multi-step workflows with external APIs, things start getting weird and complex.
State management becomes a problem. Memory retrieval is inconsistent. Latency compounds with every step. And debugging is painful because you are not tracing a single function, you are tracing decisions across a system.
What helped was thinking in layers. Input handling, planning, execution, feedback. Once I separated those, it became easier to isolate failures. Also realized that most inefficiencies come from unnecessary model calls, not the model itself.
Another thing people don’t talk about enough is cost scaling. Token usage is manageable early on, but once workflows get deeper, it adds up fast if you are not controlling context and step count.
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