AI Engineering Blog
Plain-language guides to Prompt Engineering, Loop Engineering and Graph Engineering.
What is Prompt Engineering?
Prompt Engineering is the practice of designing precise instructions so a language model returns reliable, high-quality output on the first attempt.
5 min read
Read article Loop EngineeringWhat is Loop Engineering?
Loop Engineering turns a single prompt into a self-improving agent that plans, executes, validates, improves, and retries until it meets a defined success condition.
6 min read
Read article Graph EngineeringWhat is Graph Engineering?
Graph Engineering is the emerging practice of designing AI-agent workflows as explicit graphs: nodes that work, edges that route, shared state, validators, approvals, and observability.
8 min read
Read article GuidesPrompt vs Loop: when to iterate
A practical comparison of single-shot prompting and iterative agent loops, with the exact signals that tell you to upgrade.
4 min read
Read article GuidesLoop vs Graph: when one agent is not enough
How to know that your loop has become a system, and how to decompose it into a multi-node graph without over-engineering.
5 min read
Read article GuidesHow AI Agents Work
Models, tools, memory, and control flow — the four parts of every agent, explained without hype.
6 min read
Read article Graph EngineeringBuilding Multi-Agent Systems
Design patterns for coordinating specialised agents: supervisor, pipeline, debate, and approval-gated publishing.
7 min read
Read article GuidesLangGraph Guide
How graph designs map onto LangGraph in Python and TypeScript: state, nodes, conditional edges, and checkpoints.
6 min read
Read article GuidesCrewAI Guide
Turning graph nodes into CrewAI agents and tasks, and where the two models diverge.
5 min read
Read article GuidesOpenAI Agents SDK Guide
Handoffs, guardrails, and tracing — mapping a validated graph onto the OpenAI Agents SDK.
5 min read
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