1. **Analyze the Request:** * **Role:** News summarization expert. * **Task:** Summarize the provided article concisely. * **Length Constraint:** 2-4 sentences. * **Content Focus:** Most important information. * **Negative Constraint 1:** DO NOT copy and paste. * **Negative Constraint 2:** DO NOT simply restate the title. * **Output Constraint:** Answer in English. * **Input:** Title ("How to start coding with AI agents"), Source ("hackernews"), Article Text (Just the title and "Summary:" header, implying the content is missing or this is a meta-test). 2. **Analyze the Input Data:** * The user provided a Title: "How to start coding with AI agents". * The user provided a Source: "hackernews". * The user provided "Article Text", but the text following "Article Text:" is literally just the title repeated and then "Summary:". * *Crucial realization:* There is no actual article body to summarize. The input looks like a prompt template where the content was accidentally omitted or this is a trick question. 3. **Evaluate the situation:** * I cannot summarize content that doesn't exist. * However, usually in these scenarios, if the user *says* "Article Text:" and provides nothing, or repeats the title, I have to assume they made a mistake or I need to act based on what's there. * *Wait, let me look closer at the prompt structure.* * "Article Text: How to start coding with AI agents Summary:" * It seems the user pasted a truncated version. There is no actual text describing *how* to start coding with AI agents. * *Strategy:* Since I cannot invent
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[ Product Academy](https://www.paralect.com/academy) by [](https://www.paralect.com/) Portfolio Resources AI Tools [Team](https://www.paralect.com/about-us) [Pricing](https://www.paralect.com/pricing) [Start Building](https://www.paralect.com/build) [Product Engineer](https://www.paralect.com/academy/696bd8efee989c0036a29c2b) / [Guides](https://www.paralect.com/academy/696bd8efee989c0036a29c2b)  # How to start coding with AI agents All you need to know to use AI effectively as a software engineer in 2026. * * * You probably hear about AI agents everywhere these days. The news about AI are overwhelming, yet many engineers haven't switched to agentic coding yet, or even tried it out. Below is a full guide to get you started coding with AI agents and understand why it's really a big deal. ## What is "agentic coding" anyway? Agentic coding means talking with AI through a chat UI and seeing it implementing and running code for you. Just like if you hired a developer who works for you and livestreams all the results. Agents have access to your system and can run bash commands for you, such as `npm install`, `chrome http://localhost:5173`, `git push`, or `rm -rf /`. Previously, AI was mostly used as a tab-completion tool for coding. But **in the last months of 2025** , LLM models became much better and more powerful, and now can produce great code and are super useful to iterate faster and build quality products. Now you can actually build a production-level system if you approach “vibe-coding” as an engineering task, but not as some out-of-control slop beast. World-class engineers code with AI: Linus Torvalds (Linux Creator), DHH (Ruby on Rails creator / Basecamp founder), Gergely Orosz (running the large engineering newsletter) and so many others. Huge companies like Stripe already merge [thousands of AI-generated PRs](https://x.com/stripe/status/2024574740417970462) per week. It's now a matter of time until it comes to you, so you better start early. ## Quality depends on you 🫵 AI won’t magically write a perfect project for you right away (yet). You need to configure it. Agents are capable of working on complex tasks and projects, following comprehensive coding guidelines to produce lots of quality code faster. But the style and output quality depend on you, as you need to configure your agent’s context to create exactly what you need. This is hard to do if you don’t follow a system in your code, but luckily you can use open-source configurations from others, e.g. [React Best Practices](https://github.com/vercel-labs/agent-skills/tree/main/skills/react-best-practices/rules) from Vercel. If you don’t have any rules or prompt system, chances are you’ll end up with slop. But now it’s on you, not AI. > _Slop — badly produced, generic AI output, that is not refined by a human. > See also: _[ _GIGO_](https://en.wikipedia.org/wiki/Garbage_in,_garbage_out)  ## Engineer the Context [Context Engineering](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) is the process of loading information into your AI agents. The thing is: LLMs are limited in the context size that they can process, and their output becomes much worse if you use it unwisely. Also, it costs money. The art of engineering today is the art of creating the best context architectures and writing thoughtful rules. Learn to write less code, but start writing more context-enhancing documents on **how to write code**. Use Skills, Rules, and Commands to automate common actions and enforce coding guidelines. Write down rules in Markdown as soon as you repeat something twice to an agent. This is a new DRY. Subagents let you automate multiple roles, e.g. front-end, back-end or tech writer. You can create different context directories per agent to fine tune common operations. You can tag a specific agent to activate it and run them in the background or foreground depending on the task. This way you’re kinda working with a team of developers who follow your instructions. Discover 30k+ skills [here](https://skills.sh/). ## Best practices * Write common knowledge about your project to **AGENTS.md**. This file is loaded in context every time you chat with your agent. Learn [how to write](https://www.hlyr.dev/blog/writing-a-good-claude-md) a good agents file. Be [thoughtful](https://x.com/_philschmid/status/2026354033418547444?s=20): an overloaded agents file leads to worse results. * **Break your feature into the smallest tasks and execute them one by one** , step by step, instead of trying to execute a massive project at once. Learn to write clean PRD files with acceptance criteria. Attach requirement files instead of sending long chat messages. * **Separate planning and execution**. No need to waste tokens and your cognitive power to realise that AI got your instructions wrong, or they were incomplete. Always as