Harness Engineering: What We Learnt Building AI Agents
Harness engineering is choosing how much of an AI agent's loop to make deterministic. What we learnt doing it on three of our own agents, and where to start.
Dr. Phil Winder
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13 minArticles in this topic
Dr. Phil Winder
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13 min
Copilot Studio vs Azure AI Foundry vs Build: What One Document Costs
Copilot Studio vs Azure AI Foundry vs building your own, costed on one real 236-page report: $725 a document against $37, and where the line falls.
Dr. Phil Winder
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17 min
vLLM vs Ollama vs SGLang: LLM Inference Compared 2026
vLLM vs Ollama vs SGLang on one H100 with Qwen3.8-27B and Llama 3.1 8B: throughput, latency and cost per million tokens, harness included.
Dr. Phil Winder
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11 min
DeepSeek Harness vs OpenCode: Which One We Run in 2026
DeepSeek Harness vs OpenCode 2.0, compared from running both in our own fleet: architecture, model support, cost, containment and which one suits a team.
Dr. Phil Winder
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13 min
A Comparison of AI Agent Harnesses in 2026
A comparison of AI agent harnesses in 2026: Claude Code, Codex, OpenCode, Qwen Code, DeepSeek Harness, Goose and Zed Agent, plus when you need a framework.
Dr. Phil Winder
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17 min
How to Build an AI Agent in 2026: Frameworks and Working Code
How to build an AI agent in 2026: Pydantic AI vs LangGraph vs build-your-own compared, two worked examples with real code, and the evaluation and memory patterns that survive production.
Dr. Phil Winder
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11 min
RAG vs Fine-Tuning in 2026: A Decision Framework for LLM Teams
An opinionated 2026 decision framework for choosing between retrieval-augmented generation (RAG), fine-tuning, and hybrid approaches for LLM applications. Decision tree, comparison table, and named tooling.
Dr. Phil Winder
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9 minNewsletter
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Case studies and industry analysis from our team. No hype, roughly monthly.