1. **Analyze the Request:** * **Role:** News summarization expert. * **Task:** Summarize the provided article concisely. * **Constraints:** * Length: 2-4 sentences. * Focus: Most important information. * Prohibited: Copy-pasting, restating the title. * Requirement: Add new information beyond the title. * Language: English. * **Input:** * Title: AI Harness Engineering * Source: Hacker News * Article Text: "AI Harness Engineering\n\nSummary:" (Wait, the input text is extremely sparse. It just repeats the title and says "Summary:"). This looks like a truncated input or a prompt template where the actual article text is missing. 2. **Analyze the Input Data:** * Title: AI Harness Engineering * Body: There is essentially no body text provided. It says "Article Text: AI Harness Engineering Summary:". * *Self-Correction/Realization:* I cannot summarize an article that doesn't exist in the context. The user provided the title and then what looks like a header/footer or a cut-off text. * *Hypothesis 1:* The user forgot to paste the actual article. * *Hypothesis 2:* The user wants me to hallucinate/invent a summary based on a topic I might know from general knowledge (risky, usually not what's wanted). * *Hypothesis 3:* The text provided *is* the prompt for a different task, or the user made a mistake. *Decision:* Since I am an AI, I must address the fact that the text is missing. However, looking closely at the prompt "Article Text: AI Harness Engineering\n\nSummary:", it seems the text provided is just
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Harness Engineering It was very interesting to read OpenAIâs recent write-up on âHarness engineeringâ which describes how a team used âno manually typed code at allâ as a forcing function to build a harness for maintaining a large application with AI agents. After 5 months, theyâve built a real product thatâs now over 1 million lines of code. The article is titled âHarness engineering: leveraging Codex in an agent-first worldâ, but only mentions âharnessâ once in the text. Maybe the term was an afterthought inspired by Mitchell Hashimotoâs recent blog post. Either way, I like âharnessâ as a word to describe the tooling and practices we can use to keep AI agents in check. The OpenAI teamâs harness components mix deterministic and LLM-based approaches across 3 categories (grouping based on my interpretation): - Context engineering: Continuously enhanced knowledge base in the codebase, plus agent access to dynamic context like observability data and browser navigation - Architectural constraints: Monitored not only by the LLM-based agents, but also deterministic custom linters and structural tests - âGarbage collectionâ: Agents that run periodically to find inconsistencies in documentation or violations of architectural constraints, fighting entropy and decay They also highlight how iterative this is: âWhen the agent struggles, we treat it as a signal: identify what is missing â tools, guardrails, documentation â and feed it back into the repository, always by having Codex itself write the fix.â All of the described measures focus on increasing long-term internal quality and maintainability. What I am missing in the write-up is verification of functionality and behaviour. Leaving that gap aside, and assuming we can trust OpenAIâs representation of the success of this (with respect to the author and the team, OpenAI do have a vested interest in us believing in AI-maintainable code) â here are my thoughts on what is in the article. Harnesses - the future service templates? Most organizations have just two or three main tech stacks â not every application is its own snowflake. The article made me imagine a future where teams pick from a set of harnesses for common application topologies to get started. This evokes todayâs service templates, which help teams instantiate new services on a âgolden pathâ. Will harnesses â with custom linters, structural tests, basic context and knowledge documentation, and additional context providers â become the new service templates? Will teams use them as a starting point, then shape them over time for their applicationâs specifics? With service templates, teams contribute back as they gain experience, then other teams often struggle to incorporate updates. Would we see a similar forking and synchronization challenge with harnesses? The article also made me revisit some of my older hypotheses: The runtime has to be constrained for more AI autonomy? A lot of early and current AI coding hype assumes LLMs will give us unlimited flexibility of the target runtime. Generate in any language, any pattern, without constraints â the LLM will figure it out. But for maintainable, AI-generated code at scale that we can trust, something has to give. The harness described suggests that increasing trust and reliability required constraining the solution space: specific architectural patterns, enforced boundaries, standardized structures. That means giving up some âgenerate anythingâ flexibility for prompts, rules, and harnesses full of technical specifics. A convergence on a limited number of tech stacks and topologies? As coding becomes less about typing code and more about steering its generation, AI might push us toward fewer tech stacks. Usability of frameworks and SDKs still matters â weâre seeing repeatedly that whatâs good for humans is good for AI. But developer tastes will matter less at that level of detail. Little inefficiencies and idiosyncracies in interfaces will be less annoying since we donât deal with them directly. We might choose stacks with good harnesses available and prioritize âAI-friendlinessâ. This might apply not just to tech stacks, but also to codebase structures and topologies. We might default to structures that are easier to maintain with AI because theyâre easier to harness. The OpenAI team discusses architectural rigidity and enforcement rules. The main focus areas I can see are keeping data structures stable and defining and enforcing module boundaries. Sounds reasonable â but without concrete examples, Iâm still struggling to imagine what âwe require Codex to parse data shapes at the boundaryâ looks like in practice in their harness. But if we can figure out widely how to harness codebase design patterns, will these topologies become the new abstraction layer, not natural language itself like so many AI enthusiasts hope? Two future worlds: Pre-AI vs post-AI application maintenance? Say we develop good harnessing techniques to turn AI autonomy up to 9 and increa