"The way we direct AI coding agents has changed significantly over the past couple of years. Early on, the interaction was purely conversational. You'd open a chat, explain what you wanted, provide whatever context seemed relevant, and hope the model could work with it. If it got something wrong or went down the wrong path, you'd correct it and try again. It worked, but it was ad hoc. Every session started from scratch. Every conversation required re-establishing context."
"What's happened since then is a steady progression toward giving agents more structured, persistent knowledge to work with. Each step in that progression has made agents meaningfully more capable, to the point where they can now handle tasks that would have been unrealistic even a year ago. I've been putting these capabilities to work on a specific challenge: getting an AI to author interactive workshops for the Educates training platform."
Interactions with AI coding agents moved from one-off conversational prompts to structured, persistent context that agents automatically pick up. Agent steering files provide standing instructions about project structure, conventions, and tools, removing the need to re-explain codebases each session. Protocols like the Model Context Protocol extend agent capabilities by allowing API calls, database queries, and document retrieval rather than only file access. Each advancement has increased agent capability, enabling them to perform tasks that were previously unrealistic. These capabilities are being applied to author interactive workshops for training platforms, leveraging persistent knowledge and external tool access.
Read at Grahamdumpleton
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