Building Effective AI Agents
The clearest writeup of when to reach for a workflow versus an agent. The whole DreamerOS engine-selection layer is downstream of this distinction.
The papers, repos, and guides we cite when we build.
The papers we point teams to when they ask how the constellation actually works.
The clearest writeup of when to reach for a workflow versus an agent. The whole DreamerOS engine-selection layer is downstream of this distinction.
The complement piece. Patterns for tool use, memory, and multi-agent coordination at production scale.
The pattern most modern agents are still running. Reason about the next action, take it, observe, repeat.
Why "think step by step" started showing up everywhere. The paper that made reasoning a first-class prompt structure.
Branching beats linear when the problem has more than one promising path. The intellectual ancestor of Mind-Eye depth calibration.
Agents that reflect on their own failures and update. The core idea inside IFP-Verify and the receipts log.
The honest tour of RAG. What works, what does not, where most teams over-engineer it.
The code we read first when we want to learn how a piece of the stack actually behaves.
Working code for tool use, prompt caching, batch, citations, and vision. Read before you build.
The reference patterns for function calling, embeddings, and assistants. Useful even if you do not ship on OpenAI.
The framework you should read to understand the abstractions, then decide whether you want them.
The retrieval-first cousin to LangChain. Strong on data connectors and indexing primitives.
Reference implementations for Model Context Protocol servers. The plumbing that makes tool use portable across engines.
The long-form writeups we hand to teams who want one document to read this weekend.
The companion essay to the agents paper. The framing we keep coming back to.
How to use Claude Code without wasting tokens on the wrong things. The CLAUDE.md and skills pattern starts here.
The other side of the same coin. Good on evaluation, less opinionated than Anthropic's piece.
The most enterprise-shaped of the three. Strongest on planner-executor architectures and orchestration.
The six lenses we put on every engagement. Reverse Math, Sideways Thinking, Triple-I, Anti-Slop Check, Triangulation, Fingerprint Portability.
The longer reads. Worth a flight, worth a weekend.
The textbook to read if you want to stop nodding at words like transformer and start understanding them.
The book on building production AI systems. Evaluation, deployment, monitoring, the boring parts that decide whether you ship.
The predecessor. Same author, same discipline, more on data and training pipelines.
The talks worth the hour. Bring coffee.
The graduate seminar that keeps inviting the people who built the systems to come explain them.
The clearest public window into how a frontier lab thinks about what is going on inside the model.
The one-hour talk we send to every new client. Still the best plain-English explainer of the stack.