Category
Move from prompting to collaboration: agents, context, tools, workflows, and the judgment that holds it together.
7 posts in this category
I started by asking what Jev actually built. A four-subagent RAG example brought the opportunity into focus: useful decisions need useful context and software that knows what to do next.
By Kingsley Ijomah
The valuations attached to frontier-model companies will not rise forever. If they correct, the useful question for engineers is not whether AI disappears, but which parts of the stack remain worth understanding.
By Kingsley Ijomah
I kept rebuilding the same agent inside different products. Stripping it back to one definition, three skills, one MCP tool and a visible loop showed me what an agent framework actually does.
By Kingsley Ijomah
I first read ‘Agents API’ as another agent-building SDK. The important change is that OpenAI now operates the Codex harness itself, including durable sessions, context compaction, recovery, sandboxes and subagents. Here is what that means for developers.
By Kingsley Ijomah
I started by comparing model providers, clouds, data platforms and agent frameworks as if they were fighting for the same prize. They are not. The more useful question is which control point each vendor wants to make hardest to replace.
By Kingsley Ijomah
When I first looked beneath a working RAG demo, I learned that retrieval quality depended on much more than a vector database. I built this local, inspectable project so you can follow the same path through chunks, embeddings, conflicts, deletion and evaluation.
By Kingsley Ijomah
I understood building with AI more clearly when a trend-detection project stopped being an LLM problem and became a system-design problem involving embeddings, time, evaluation, incomplete data and human judgement.
By Kingsley Ijomah
