Do the Simplest Thing That Works

THE QUESTION THIS PAGE ANSWERS

ANSWER FIRST

Do the Simplest Thing That Works?

Anthropic's core engineering philosophy: "Do the simplest thing that works"

DECISION RULE

Make the claim earn its place. Use this page as a decision aid, not a definition to memorize. Connect the idea to one real task, one observable result, and one failure that would change your mind.

TRY NEXT

Write one question you could answer with evidence after trying this idea.

WATCH FOR

A conclusion that sounds complete but leaves the key assumption untested.

"Do the simplest thing that works"

AI Agent Engineering Practice

Three Core Lessons from the Claude Code Source

The Heart of an Agent Is State Management, Not Intelligence

Every Agent engineering problem ultimately reduces to one question: what information appears in the context window, when, and in what form. Model intelligence is baked in through pre-training — you can't control it. But context construction, pruning, and arrangement are decisions engineers can make.

Most of Claude Code's engineering complexity lies in meticulously managing context: what to include, what to remove, when to compress, when to reset. Making the model smarter is actually secondary. Context Engineering is the core competency of Agent engineering — far more than a nice-to-have.

A Harness Encodes Assumptions — Assumptions Expire

Every line of scaffolding you write today implicitly assumes something about the current model's capabilities. When the model is upgraded, those assumptions may all become invalid.

Real-world case: Claude Sonnet 4.5 exhibited context anxiety — performance degraded noticeably as conversations grew longer. The team added a context reset mechanism to periodically compress context. When they later switched to Opus 4.5, the anxiety disappeared — and the context reset had become a drag on efficiency. Lesson: the scaffolding you write today may need to be thrown away tomorrow.

Models Are Getting Stronger; Your Engineering Is Getting Simpler

More and more auxiliary logic (retries, error correction, formatting, context compression) will become unnecessary as model capabilities improve. The best engineering decision is: don't write code today that you might not need tomorrow.

This doesn't mean engineering isn't needed. On the contrary, understanding which logic will become obsolete as models advance, and which are truly durable architectural decisions — that judgment is the most important engineering skill. Sandbox isolation, permission layering, and evaluation frameworks won't expire; but specific Prompt tricks and model-specific workarounds might be unnecessary in six months.

FULL JOURNEY

Complete Review: Four Parts

PART 1

Understanding What LLMs Are

From the Transformer's attention mechanism to Token economics, from training to emergent capabilities. LLMs are probabilistic models with clear capability limits — far from all-knowing black boxes.

PART 2

Learning to Work with LLMs

From Prompt Engineering to Few-shot Learning, from RAG to Function Calling. Master the methodology for collaborating with LLMs and make them your force multiplier.

PART 3

From Demo to Product

Bridging the gap from "it runs" to "it's usable." Cost optimization, latency control, evaluation frameworks, safety and compliance — turning an AI demo into a reliable production product.

PART 4

From Product to Design Patterns

Five Workflow types + autonomous Agents, context engineering, tool design, long-running architectures, and secure containerization. Master the core design patterns of the Agent era, grounded in open-source engineering practice.

These design patterns are not the destination — they will evolve as the next generation of models arrives.
But understanding the thinking behind them is the truly transferable capability.

ACKNOWLEDGEMENT

The advanced portion of this course is based on the publicly available Claude Code source code and engineering blog posts.

Course Home

Why “Three Core Lessons from the Claude Code Source” can find relevant content

“Anthropic's core engineering philosophy: "Do the simplest thing that works"” moves retrieval beyond storing material: the real question is how to find what is relevant. That decision shapes the input quality of RAG, recommendation, and image-search systems.

Similarity is not the answer

In the flow described by “Anthropic's core engineering philosophy: "Do the simplest thing that works"”, embeddings place items in a comparable semantic space and a neighbor index narrows the search. The final answer still depends on whether the retrieved chunks cover the question, whether the distance metric fits, and whether the evidence is current.

Separate findable from relevant

Turn “Anthropic's core engineering philosophy: "Do the simplest thing that works"” into a small test: prepare queries with known answers, record relevance, misses, and distractors, then decide whether chunking, the index, or reranking needs to change.

From “Three Core Lessons from the Claude Code Source” to “Complete Review: Four Parts”

“Three Core Lessons from the Claude Code Source” grounds the problem in “1 The Heart of an Agent Is State Management, Not Intelligence Every Agent engineering problem ultimately reduces to one question: what information appears in the context window, when, and in what form . Model i…”. “Complete Review: Four Parts” then moves it toward “PART 1 Understanding What LLMs Are From the Transformer's attention mechanism to Token economics, from training to emergent capabilities. LLMs are probabilistic models with clear capability limits — far from al…”. Together, they show that the lesson is not just a conclusion to remember, but a claim with conditions.

Carry the judgment into the next situation

The same logic applies to retrieval: define what counts as relevant, check whether recall covers the question, and then inspect whether ranking, chunking, or freshness pushed useful evidence out.

“Three Core Lessons from the Claude Code Source”: 1 The Heart of an Agent Is State Management, Not Intelligence Every Agent engineering problem ultimately reduces to one question: what information appears in the context window, when, and in what form . Model i…
“Complete Review: Four Parts”: PART 1 Understanding What LLMs Are From the Transformer's attention mechanism to Token economics, from training to emergent capabilities. LLMs are probabilistic models with clear capability limits — far from al…

The final “Finish by testing the claim” brings the discussion to “Anthropic's core engineering philosophy: "Do the simplest thing that works"”. The useful thing to carry forward is knowing which judgments must be revisited when input, scale, or risk changes.

Mark as learned Your reading progress updates automatically

ARTICLE DISCUSSION

Leave one useful thought here.

Keep the idea that clicked, the question that stayed open, or a small note for the next learner.

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Discussing Do the Simplest Thing That Works Engineering Patterns for Reliable Agents

3discussionsArticle discussion · synced with the Circle

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Asha MorganContent editorAug 20, 2026

INSIGHTField note

I turned one judgment from this article into a small experiment I could run today. Knowing what to observe next is more useful than simply remembering the conclusion.

ARTICLE DISCUSSION7 helpful

Lin HarperIndie developerAug 18, 2026

INSIGHTInsight

After reading this, I first looked for the conditions behind the idea instead of copying the method into a project. That order made the later trade-offs much clearer.

ARTICLE DISCUSSION5 helpful

Kiki MooreProduct operationsAug 15, 2026

QUESTIONQuestion

When this judgment reaches real work, which constraint should be added first? I am curious which step matters most between reading and the first practical attempt.

ARTICLE DISCUSSION4 helpful