Research briefs, brain sparks and deep dives on AI.
A map of the protocols that connect AI agents to tools, to each other, to people and to payments, as of October 2026: what each one does, how they differ, where they are weak, and how to build with them.
A map of retrieval and memory for agents as of October 2026: why the context window is not enough, how retrieval and memory systems work, what the benchmarks show, and how to choose an architecture for an agent of your own.
How LLM applications fail after launch, and what catches it: tests, release gates, runtime checks, tracing and online evaluation, with the standards and tools as of October 2026.
A map of agent frameworks as of October 2026, including the AWS and Azure stacks, LangGraph and the provider SDKs: what each layer does, how they compare, and how to pick one.
A map of agent evaluation as of October 2026: the main benchmarks, how they grade, where they fail, and how to build evaluations for an agent of your own.