Building Production-Ready AI Apps

In this session, Ismail Salikhodjaev (AI Engineering Lead) walks through the architecture that makes AI-driven development trustworthy — the harness, context engineering, and the verification layers that close the gap between agent adoption and engineer trust. ───────────────────────────── 🎯 What's covered in this session: ───────────────────────────── ▸ The trust gap: 84% of developers adopt AI tools, only 29% trust the output — and why closing it requires architecture, not faith ▸ AI in product vs. AI for SDLC — two completely different architecture problems ▸ How an LLM actually works: stateless models, the context window as working memory, and what makes an LLM an agent ▸ Tools, MCP (Model Context Protocol), and why the action space you grant is the trust surface you own ▸ Context engineering as a discipline: the four failure modes (lost in the middle, context rot, poisoning, drift) and the Write / Select / Compress / Isolate scaffold ▸ The harness: context files, skills, sub-agents, hooks, slash commands, memory — each one a trust mechanism ▸ The verification ladder: mechanical, agentic, behavioral, and human gates ▸ Live demo: building a harness in Claude Code ───────────────────────────── 🔗 Links: ───────────────────────────── ▸ Program website: https://www.ai-incubator.org/ ▸ Telegram channel: https://t.me/ai_incubator_org ▸ Speaker: https://www.linkedin.com/in/ismailsaleekh/ ───────────────────────────── Session 3 of 16 | Week 1: Foundations | AI Incubator Mentorship Program