Disciplined Vibes: Scaling AI-Assisted Engineering (By: Sheharyar Nasser) - Build with AI 2026

Talk by Sheharyar Naseer (https://www.linkedin.com/in/sheharyarn/) at Build with AI 2026 by GDG Lahore

Transcript

1. Code District, Lahore Disciplined Vibes: Scaling AI-Assisted Engineering Sheharyar Naseer ◦ June 2026
2. Sheharyar Naseer Systems architect & technology advisor for startups and enterprises. Find me online @sheharyarn
3. Background Principal Software Architect at Infra One ✦ Worked with: Apple, Slab, TheScore, Superlist, etc. ✦ 16+ years of polyglot experience, focus on Web & Cloud ✦ StackOver ow: 75,000+ score (Top 5 in Pakistan) ✦ Author / Contributor of multiple famous libraries & tools ✦ Featured on popular developer communities fl ✦
4. PA R T 1 The Problem PA R T 2 It's Not the Model PA R T 3 Harness Engineering PA R T 4 Outline Live Workshop OU T R O What's Next?
5. 01 The Problem Why you're struggling with AI-assisted coding and the data backs it up.
6. Struggling with AI 16+ years of experience, still humbled by a chatbot ✦ Struggled a lot with AI-assisted coding ✦ Code quality was extremely poor ✦ Often had to spent time xing it ✦ Or throwing it away and doing manually fi ✦
7. AI-Assisted Problems ✦ Hallucinated APIs, function calls, and packages ✦ Insecure code ✦ Architectural drift ✦ Ignored edge-cases ✦ Incorrect, or no error-handling ✦ Performance issues ✦ So many more...
8. The Data Agrees METR DORA METR's randomized controlled trial found experienced developers were 19% slower with early-2025 AI. Google's DORA 2024 research found AI adoption reduced delivery stability, continuing into 2025 despite higher adoption & throughput. SOURC E S O UR CE
9. “ Seniors often get worse results than juniors from same tools until they learn deliberate prompting. But once they do they have a massive advantage. Sabrina Goldfarb SWE at Github Co-Pilot
10. 02 It's Not The Model Exploring the root causes and developing the right thinking model.
11. It's a You Problem ✦ Don't understand how LLMs work ✦ ✦ fi ✦ Gold sh memory & context management Incomplete specs ✦ Basic prompts ✦ Missing documentation & examples Unreliable guardrails ✦ No systems or quality checks ✦ Agents don't receive feedback about what's wrong
12. Mental Models AI Search Vibe Coding Vibe Engineering Shallow use of modern LLMs as a Google replacement Fully delegating code to AI without reviewing output Accelerating professional software engineering with AI YO U AR E HE RE
13. Vibe Engineering ✦ Does not mean better prompts ✦ Foundation/architecture/system where the agent can "succeed" ✦ Feedback loops ✦ Also called Evaluation Driven Development (EDD)
14. “ You shouldn’t be prompting coding agents anymore. You should be designing loops that prompt your agents. Peter Steinberger Creator of OpenClaw, Technical Staff at OpenAI
15. 03 Harness Engineering The scaffold is the product.
16. What's a Harness? ✦ LangChain research team describe it as: Agent = Model + Harness ✦ "Everything other than the model" ✦ Prompt, Evals, Tool Calls, Docs, Context, etc. ✦ Even the GUI/CLI "agent" tool you use “ Agent = Model + Harness Vivek Trivedi (Researcher, LangChain)
17. The Model Doesn't Matter ✦ SWE bench score improvements ✦ 42% → 78%, 46% → 80%, 23% → 45% ✦ ~22 point swings vs ~1 point swings ✦ Using frontier models S A ME HA R N ES S Different Model S A ME MO DE L Scaffold Changes ~1 PO IN T SW IN GS ~22 PO IN T SW IN GS
18. Anatomy of a Harness ✦ ✦ Inner Harness (System) ✦ Built into your coding agent (CLI/GUI tool) ✦ System prompt, Tool calls, Orchestration M O DE L Outer Harness (User) I N N E R H ARN E SS ✦ Controls put in place by users ✦ User prompt, Agent rules, Output validation ✦ Our focus today O UTER HARNESS
19. Types of Harness DIRECTIO N DO MAIN NATURE Feedforward Guides BEH AVI O UR I N F E RE N TI AL Feedback Sensors M AI NTAIN ABI L ITY ARC H I TECTURE DETERMINISTIC
20. Types of Harness DIRECTIO N DO MAIN NATURE Feedforward Guides BEH AVI O UR I N F E RE N TI AL Feedback Sensors M AI NTAIN ABI L ITY ARC H I TECTURE DETERMINISTIC
21. Types of Harness DIRECTIO N DO MAIN NATURE Feedforward Guides BEH AVI O UR I N F E RE N TI AL Feedback Sensors M AI NTAIN ABI L ITY ARC H I TECTURE DETERMINISTIC
22. Types of Harness DIRECTIO N DO MAIN NATURE Feedforward Guides BEH AVI O UR I N F E RE N TI AL Feedback Sensors M AI NTAIN ABI L ITY ARC H I TECTURE DETERMINISTIC
23. Types of Harness DIRECTIO N DO MAIN NATURE Feedforward Guides BEH AVI O UR I N F E RE N TI AL Feedback Sensors M AI NTAIN ABI L ITY ARC H I TECTURE DETERMINISTIC
24. PRO M PTS AGEN TS.M D SPEC S, PRD & ADR STYL EGUI DES REF EREN C E DO CS RUL ES SC RI PTS / CL I TO O LS CO DEM O D S L AN GUAGE SERVERS ... Feedforward Guides I NI TIAL G E N E RATI O N  ✦ HUMAN AGEN T UNIT TESTS E2E TESTS STATI C AN ALYSI S REVI EW AGEN TS LO G S BROWSER L I N TERS SBO M VAL I DATI O N SEC URI TY SCAN N ERS ... Feedback Sensors SEL F - C O RRECTI N G
25. Implementing Guides ✦ ✦ . . . . . . ✦ Write actual documentation ✦ Guides, rules, conventions; plus examples ✦ Current architecture overview ✦ Long-term specs, PRDs, and ADRs Add helpful tooling ✦ Code generation scripts, tools, helpers ✦ Language servers Entrypoint is the "router" my_app ├── AGENTS.md ├── docs/ │ ├── rules/ │ ├── guides/ │ ├── adrs/ │ └── specs/ ├── └──
26. Implementing Sensors ✦ More important than Guides ✦ ✦ For maintainability and architectural quality Focus on Deterministic controls rst ✦ I M PLE M ENTATIO N L AYE RS 1. L I N TI N G & STATI C C H EC KS 2. UNIT TESTS Fast, reliable, cheap 3. I NTE G RATI O N/ E2 E Implementation Layers 4. AI REVI EWS ✦ Fastest & accurate feedback early ✦ Goal: Push agents' reliable coverage as far up as possible fi ✦ 5. MANUAL Q A
27. 04 Live Workshop It's your turn. Let's build our own outer harness.
28. Pick an Idea Lets' build the App and a Harness
29. 05 What's Next? Improving harnesses and building repeatable systems.
30. Recommendations ✦ ✦ ✦ Establish Discipline ✦ Capture standard conventions, security mandates, architecture patterns ✦ Keep AI out of writing tests, preserve double-bookkeeping Build Reusable Harnesses ✦ CI templates with common deterministic checks ✦ Inferential review agents for security, architecture, gap analysis, even PR reviews Scale via Service Templates ✦ Service-level AGENTS.md
31. Service Templates ✦ Enterprises & agencies have pre-de ned service templates ✦ Internal team guides ✦ Codemods & internal tools ✦ Boilerplate projects Embed harnesses directly in them ✦ Scaffold not just code, but AI knowledge and conventions from day one ✦ Inter-organization review agents fi ✦
32. Advanced Workflows ✦ Custom skills and slash commands ✦ Subagents for sub-tasks for context optimization ✦ Agent Councils & Consensus ✦ ✦ Parallel agent execution with git worktrees ✦ ✦ Adverserial reviews with multiple agents deciding on next steps Multiply output using same harness Independently running agent loops ✦ Spec → Code → PR → Review → Address Feedback → Merge
33. Questions? Further Reading → martinfowler.com/articles/harness-engineering.html These Slides → shyr.io/t/disciplined-vibes More Talks → shyr.io/talks  hi@shyr.io  @sheharyarn  shyr.io