--- title: "Agentic Engineering Patterns" section: "raw" type: "source" created: "2026-08-27" updated: "2026-08-27" canonical: "https://pyweb.dev/wiki/raw/articles/simon-willison-agentic-engineering-patterns-2026" --- # Agentic Engineering Patterns By Simon Willison (2026). A comprehensive guide and pattern catalog for getting reliable, high-quality results from coding agents such as Claude Code and OpenAI Codex. ## Core Extraction Summary ### 1. Named Frameworks & Patterns (Author's Exact Words) - **Agentic Engineering Patterns**: Discipline of applying structured software engineering principles to AI coding agents. - **"First run the tests"**: Four-word prompt convention used when starting any session on an existing project. - **"Use red/green TDD"**: Four-word prompt instructing the agent to author a failing test before writing implementation code. - **"Agentic manual testing"**: Teaching agents to perform exploratory, stateful testing using terminal commands, browser automation, and visual screenshots. - **"Writing code is cheap now"**: The realization that code generation cost has collapsed, making the cost of testing and proof negligible. - **"Hoard things you know how to do"**: Maintaining personal libraries of patterns, cookiecutters, and recipes for agents to recombine. - **"AI should help us produce better code"**: Reframing AI not as a shortcut to cut corners, but as leverage to write better-tested, better-documented software. - **"Compound engineering loop"**: Iterative workflow where every agent mistake and edge case is codified into tests, documentation, or tool harnesses. - **"Linear walkthroughs" & "Interactive explanations"**: Using agent-generated tools (Showboat, Present) to force human understanding of codebases. - **"Explore subagent" & "Specialist subagents"**: Delegating bounded exploration or verification tasks to isolated agent instances. ### 2. Decision Rules - **When starting a session against an existing repo**, prompt `First run the tests` (or `pytest`), **because** it forces the agent to locate the test runner, discovers project shape and size, and primes a testing mindset. - **When implementing any non-trivial feature or bug fix**, prompt `Use red/green TDD`, **because** writing the failing test first proves the test is sensitive to the bug and prevents false-positive test passes. - **When validating user interfaces or CLI tools**, combine automated tests with agentic manual testing (screenshots, CLI runs with output capture), **because** automated tests frequently pass on mock data while UI/runtime integration is broken. - **When an agent makes a mistake**, immediately update project documentation or tests, **because** agents reuse existing patterns and prompt history in the repo. ### 3. Anti-Patterns & Failure Mechanisms - **"Inflicting unreviewed code on collaborators"**: Dumping large, untested, agent-generated PRs onto colleagues or maintainers, offloading the cognitive burden of verification. - **"Skipping manual verification due to green automated tests"**: Relying solely on unit tests that may test the wrong assertions or happy paths while edge-case behavior fails. - **"Blind code acceptance"**: Accepting multi-file diffs without reading or executing them, leading to rapid technical and comprehension debt. ### 4. Quantitative Claims & Qualifiers - Four-word prompts (`First run the tests`, `Use red/green TDD`) trigger substantial pre-trained software engineering discipline baked into foundation models. - Automated tests that previously took hours to write and maintain now take "just a few minutes" with an agent. ### 5. What the Source Does NOT Claim - Does **NOT** claim that coding agents make software engineering discipline obsolete; claims that agentic tooling makes rigorous testing and verification mandatory and frictionless. --- ## Guide Structure & Full Outline The guide is organized into five main sections: 1. **Principles**: - *What is agentic engineering?*: Distinguishing structured agentic workflows from undisciplined "vibe coding". - *Writing code is cheap now*: Good code still has a cost in comprehension and maintenance; new habits are required. - *Hoard things you know how to do*: Recombining proven patterns with agent velocity. - *AI should help us produce better code*: Using agents to avoid technical debt, explore more architecture options, and embrace compound engineering. - *Anti-patterns*: Avoiding unverified code dumps on maintainers and teams. 2. **Working with Coding Agents**: - *How coding agents work*: LLMs, chat-templated prompts, token caching, tool calling loops, system prompts, reasoning. - *Using Git with coding agents*: Core concepts, atomic branching, history rewriting. - *Subagents*: Claude Code Explore subagent, parallel subagents, specialist subagents. 3. **Testing and QA**: - *Red/green TDD*: Failing tests before implementation. - *First run the tests*: Priming the agent context and discovering test harnesses. - *Agentic manual testing*: Browser automation, terminal capture, Showboat notes. 4. **Understanding Code**: - *Linear walkthroughs*: Step-by-step code explanation artifacts. - *Interactive explanations*: Dynamic UI explanations of algorithms. 5. **Annotated Prompts & Workflows**: - WebAssembly GIF optimization, newsletter tool extensions, artifact prompts, proofreaders, and audio highlights. --- ## Agent Navigation ### Machine endpoints - Knowledge graph: https://pyweb.dev/api/graph.json - Graph analysis: https://pyweb.dev/api/graph-analysis.json - Context index: https://pyweb.dev/llms.txt