Episode 4: Need analysis and agentic workflow design
The fourth episode of our Agentic AI and agentic workflows series, on how to tell whether an agent is the right tool, how that answer shapes architecture from simple automation to advanced agents, and why workflow structure, decision boundaries, and memory strategy drive reliability at scale.
In this episode we explore what it really takes to design agentic AI systems that work in the real world, not just in demos. We center the conversation on why thoughtful planning matters, and how skipping early analysis often leads to fragile or overly complex solutions. We walk through a practical way to evaluate whether an AI agent is even the right tool for the job, then unpack how those answers shape architectural choices, from simple automation to more advanced agent-based approaches.
Along the way we highlight how workflow structure, decision boundaries, and memory design influence reliability and scale. We give special attention to how agents access and retain knowledge over time, and why memory strategy is just as important as model selection. We also discuss the common pitfalls teams encounter when building agentic systems, including unnecessary complexity and weak safeguards, and how to avoid them before they become costly mistakes.