If you've ever seen "The Illustrated Transformer" — the diagram-heavy explainer that made attention mechanisms click for a generation of ML practitioners — you already know why Jay Alammar publishing a new guide is worth paying attention to. On September 7, 2026, reports surfaced that Alammar released a build-from-scratch AI agent guide built around roughly 300 original figures, applying the same visual-first teaching method to agent architecture that he's used for transformers and BERT.
TL;DR
| Question | Answer |
|---|---|
| Who published it? | Jay Alammar, creator of "The Illustrated Transformer" and "The Illustrated BERT" |
| What's the format? | A from-scratch AI agent guide with approximately 300 original illustrated figures |
| When did it surface? | September 7, 2026 |
| Why does his track record matter? | His transformer explainers became standard references for understanding model internals visually |
| Who is this for? | Learners and builders who want the underlying mechanics of agents, not just a framework tutorial |
| Where's the hands-on complement on explainx.ai? | Build your first agent loop, step by step |
Why Jay Alammar's name carries weight here
Alammar built his reputation on a specific, hard-to-replicate skill: taking a technically dense system — transformer attention, BERT's masked-token pretraining — and rendering it as a sequence of diagrams that build understanding step by step, without requiring the reader to first parse the original research paper's notation. "The Illustrated Transformer" in particular has been cited and linked from course syllabi, engineering onboarding docs, and countless technical blog posts (including plenty on explainx.ai) as the resource people point newcomers to before they touch the original "Attention Is All You Need" paper. He later co-authored "Hands-On Large Language Models" and worked on ML education efforts with Cohere.
Applying that same method to AI agents is a natural next step, and arguably overdue — agent systems in 2026 have become genuinely complex, spanning reasoning loops, tool calls, memory retrieval, sandboxing, and multi-agent coordination, and most existing educational material explains these pieces through code or framework documentation rather than through the kind of conceptual diagrams that make the underlying mechanics stick.
What a diagram-first agent guide can do that code can't
Code shows you exactly what happens; it's much worse at showing you why the pieces are arranged the way they are, or how state actually flows between them over time. A well-built diagram of an agent loop — the same category of visual explainx.ai uses in its own how AI agents work end-to-end guide — can show, in one glance, how a user request becomes a plan, how a plan becomes tool calls, how tool results feed back into context, and where a loop terminates or retries. That's the exact value Alammar's transformer diagrams provided for attention: not a replacement for reading the code, but a mental model that makes the code make sense once you do read it.
This distinction matters more for agents specifically than it did for transformers, because agent behavior is shaped as much by the harness wrapped around a model as by the model itself — a point Y Combinator's own harness panel made explicitly this same week, citing the same model weights scoring roughly 30% vs. 95% on ARC-AGI depending entirely on harness quality. Understanding why a harness is built the way it is — not just what API calls it makes — is exactly the kind of conceptual gap a diagram-first guide is positioned to close.
Where this fits next to explainx.ai's own agent resources
A conceptual, illustrated guide and a hands-on, code-first guide serve different but complementary purposes, and pairing them is the fastest path to actually understanding agents rather than just copy-pasting a working example. On explainx.ai:
- How AI agents work, end to end — the conceptual overview closest in spirit to a diagram-first explainer.
- How to build your first agent loop, step by step — the hands-on companion, with runnable code.
- How to build your first agent skill, step by step — extends the same fundamentals into the skills layer.
- What is an agent harness? Complete guide — the scaffolding layer a diagram of "an agent" often glosses over, but which determines most of real-world performance.
- Multi-agent orchestration patterns guide — for once a single agent loop isn't enough.
If you're new to agents, the practical sequence is: read a conceptual guide like Alammar's to build the mental model, then work through a hands-on tutorial to turn that model into working code. Neither one alone gets you all the way there.
Related on explainx.ai
- How AI agents work, end to end
- How to build your first agent loop, step by step
- How to build your first agent skill, step by step
- What is an agent harness? Complete guide
- Multi-agent orchestration patterns guide
- YC's harness panel: self-improving agents, OpenJarvis, and QM
- What are Agent Skills? Complete guide
Sources
- Reports and reactions on X citing Jay Alammar's new AI agent guide, September 7, 2026
This post reflects reporting on Jay Alammar's guide as of September 7, 2026; specific chapter contents and the exact figure count were not independently verified against the primary source at publication time — check Alammar's own site or GitHub for the guide directly.