Blog Archive
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Multi-Agent vs Single-Agent AI Systems for Software Engineering
Compare multi-agent and single-agent AI systems for software engineering, including reliability, governance, speed, and when each architecture should be used.
Everyone Wants an AI Agent. Nobody Wants to Define What It Should Do.
The most expensive mistake in AI projects isn't a technical one. It's the discovery conversation that didn't happen before anyone wrote a single line of code.
Production AI Agent Checklist: 20 Must-Haves Before You Scale
Use this production AI agent checklist to validate architecture, governance, observability, and human review controls before scaling agentic workflows.
Your Prompt Isn't the Problem. Your Architecture Is.
After reviewing dozens of failing AI integrations, the issue is almost never the prompt. It's everything around it.
Your Agent Followed Instructions. That Was the Problem.
A practical field note on why AI agent failures usually start with vague task definitions, not weak models, and how to fix that in real workflows.
AI Agent Orchestration: The Practical Playbook for Engineering Teams
A practical guide to AI agent orchestration for engineering teams: architecture, guardrails, workflows, and how Axon-style systems move from ticket to PR.
Autonomous Software Delivery Pipeline: Ticket to PR in One Flow
How to design an autonomous software delivery pipeline that moves from ticket to pull request with AI agents, quality gates, and controlled handoffs.
The Five Stages of an Engineer Accepting AI.
Denial. Anger. Bargaining. Stack Overflow still returning 404. Acceptance.
"Just a Simple Website" — A Horror Story
A field guide to the most dangerous phrase in software development, translated from Client into English.
MCP Servers for AI Agents: Why Tool Boundaries Matter in Production
A practical explanation of MCP servers for AI agents, including integration boundaries, security benefits, and why MCP improves maintainability in production.