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Graph Engineering for AI Agents: How to Set It Up with Skills
Agent loops hide the decisions that matter: what runs, what waits, which evidence counts, and what happens after a failure. Graph engineering makes them inspectable, and this guide ships eight reusable Skill templates for the core topologies.
Context
Context is infrastructure, not a prompt
The teams getting reliable output from agents stopped writing clever prompts and started engineering durable context. Here is the shape that work takes.
Context
Your AGENTS.md Is Not Documentation. It Is an Execution Contract
AGENTS.md should define the global operating contract for agent work, while routing files, stage contracts, skills, and references carry the rest of the context architecture.
Workflows
Reliable Agents Need Gates: Context, Plan, Review, Verify
One long agent chat is not an engineering process. Reliable agent work needs a loop with explicit gates, artifacts, and evidence.
Engineering
AI Does Not Fix Weak Engineering Systems. It Amplifies Them.
AI coding adoption is not a tool rollout. It is an operating-model change.
Reliability
Passing Tests Is Not Enough: The SlopCodeBench Lesson
Agent-generated code can pass today and still erode tomorrow. Teams need systems that measure maintainability, semantic fit, architecture drift, and proof of review.
Workflows
Stop Repeating Prompts. Turn Them Into Skills.
The best AI coding teams will not share prompt snippets. They will share versioned operating procedures that agents can execute.