A Technical Lead's data-driven analysis of how vibe coding accelerates technical debt at 3x the normal rate, why 95% of developers feel productive while shipping worse code, and what rescue engineering looks like in 2026.
How a non-technical business owner went from painful outsourcing cycles to building products himself with Claude, Gemini, Codex, and Google Stitch. Real workflow, real tools, real numbers.
One year after Andrej Karpathy coined 'vibe coding', the industry is moving on. Discover why vibe coding fails at scale — context collapse, cognitive debt, zero system design — and how agentic engineering is emerging as the professional discipline for AI-first software development.
A personal take on the hidden cost of AI-generated briefs. When CEOs and managers skip the reasoning process and hand developers full AI implementations as 'briefs,' they double the workload instead of halving it. The same problem plagues multi-agent AI systems — and the solution is the same: a Harness.
Make agents smarter over time with long-term memory, RAG, task reflection, and inter-agent knowledge sharing.
Build a real-time Streamlit dashboard: Kanban board, agent status, cost tracking, and human checkpoint approval.
The finale. Optimize costs, match models to agents, calculate ROI, and make your AI team production-ready.
Build the foundation: BaseAgent class, TeamState, agent config YAML, SQLite memory store, and tool registry.
Build the QC Engineer and Technical Architect agents. QC writes test cases and acceptance gates before code exists. TA produces the technical spec, architecture decisions, and implementation plan. They run in parallel — the first big optimization in our pipeline.
Build the Product Owner and Business Analyst agents. The PO clarifies requirements and defines scope; the BA breaks them into actionable user stories with acceptance criteria. Full implementation with prompts and output schemas.
Build the Senior Software Engineer agent that writes code, runs tests, and iterates until they pass.
Build the Tech Lead (code review), DevOps (CI/CD generation), and PM (coordination) agents.
Assemble all 8 agents into a single LangGraph workflow. Full E2E example from client brief to deployed config.
A hands-on account of building, debugging, and shipping real features using Claude Opus 4 as a pair programmer — including the failures, the debugging rabbit holes, and the moments where AI pair programming genuinely shines.
The vision behind building autonomous AI agents for every role in a software team: PO, BA, QC, TA, SSE, DevOps, TL, and PM. From solo developer bottleneck to an AI-powered development pipeline.
How to architect a multi-agent system using Domain-Driven Design principles. Define bounded contexts, domain events, state machines, and communication patterns for your AI software team.
A hands-on comparison of the top multi-agent frameworks: LangGraph, AutoGen, and CrewAI. We build the same PO Agent in all three to see which fits best for a software team simulation.
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