Plano Prompt Engineers (ChatGPT, OpenAI, Langchain)

🔁 Loop Engineering: Beyond The Hype

We've all become better prompt engineers.

We add context, refine instructions, experiment with models, and split work across multiple agents.

Eventually, we steer the AI toward the result we want.

But once an agent starts writing code, using tools, and making decisions, someone - or something - has to determine whether the work is safe, making progress, and actually complete.

The challenge is no longer just about generating a better response.

It's also about designing the control system around the model: one that enforces your requirements, measures its progress, and decides when the agent should continue, retry, or stop.

In this session, we'll cut through the hype and explore the engineering principles behind modern agentic loops.

We'll examine what a loop is, the core components that make them work, and how they help transform probabilistic language models into more reliable engineering systems.

You'll leave with a practical mental model for designing reliable AI control systems, along with an open source reference implementation you can explore, adapt, or use in your own projects.

We'll cover:

• Why prompt engineering and loop engineering solve different problems
• The five questions every reliable loop must answer
• Core concepts including goals, guards, gates, feedback, and stopping conditions
• Fundamental engineering patterns for building more reliable AI systems