The Good Tech Companies - Context Engineering for Coding Agents

Episode Date: October 11, 2025

This story was originally published on HackerNoon at: https://hackernoon.com/context-engineering-for-coding-agents. Context engineering for coding agents is the best way to improve the model performance for code generation. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #llms, #ai-coding-agents, #vibe-coding, #context-engineering, #coding-agents, #llm-engineering, #llm-prompting, #good-company, and more. This story was written by: @ichebykin. Learn more about this writer by checking @ichebykin's about page, and for more stories, please visit hackernoon.com. Coding agents are getting pretty good, but they're inconsistent. The same prompt can work one time and break the next. To get reliable output, you can’t just prompt better. You need to shape its inputs and give it structure.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. Context engineering for coding agents by Source Wizard. Vibe coding hype is winding down, because there are huge limitations to what can large language models achieve. Despite this coding agents are getting pretty good, they can spin up new front-end pages, wire-up APIs, and even build C, CD configs. But anyone knows how inconsistent they are. The same prompt can work one time and break the next. They forget parts of your codebase, mixed deprecated SDKs, or just make stuff up. The issue isn't that the model is bad, it's that it doesn't know your context. Here's the proof. HTTPS colon slash slash cursed lang. Org, someone wrote a programming language

purely using the coding agent. If this was possible, anything is. To get reliable output, you can't just prompt better. You need to engineer the environment the agent works in, shape its inputs, give it structure and set the right boundaries. That's what's called context engineering. One, LLMs don't think like engineers. LLMs don't understand code the way we do. They predict tokens based on patterns in the previous input, not on app builds or structure. So even small changes in the prompt or surrounding files can change the output. That's why one run passes tests and another breaks imports or forgets a semicolon. Without structure, they're like interns guessing your stack from memory. You can't fix this by yelling at the model. I can tell, I tried. You fix it by giving it

the right scaffolding, the constraints and environment that guided award valid code every time. 2. Constraints make code predictable. If you want the agent to behave consistently, constraints are your friend. They narrow the space of possible outputs and force the model to stay aligned with your setup. Some useful constraints. Types. If you're using TypeScript or JSON schemas, the agent can see exactly what shape. shapes it needs to follow. Lint plus format rules, Prettier, ESLint, or COGEN rules make the output consistent without extra prompting. Smaller tasks, instead of, build me a back end, ask, add this route to. Local scope equals fewer surprises. Configs and templates, tools like Typeconf and Varlock can

pre-defined environment variables, SDKs, or any configuration patterns the agent must follow. The trick is to make sure that you're always writing code with strong types. You don't lose flexibility, you just catch mistakes earlier and make behavior repeatable. 3. Build systems as ground truth. Even when the code looks fine, it often breaks it build time because the agent guessed wrong about how things are wired. But often the agent doesn't even understand how to build your code correctly. For example, you ask it to run tests, it writes, but your repo uses or it tries to build a Docker file that doesn't even exist your actual environment. It imports a package that isn't even installed.

The fix is to abstract the build system. Give the agent a clear picture of how code is built, tested, and deployed. Think of it as an additional agent tool for your project's environment. Once the agent knows what build means in your world, it can use that knowledge instead of guessing. Basil, Buck, NX, or even a well-structured package. Jason are already good foundations. The more you surface this info, the fewer hallucinations you'll deal with. If you want to go further, you can write your own tool hooks to prevent the agent from calling

incorrect build system, check out the Claude Code Guide. Httpskolon slash-slash-docks. Claude Com N. Docks, ClodCode, Hooks Guide. 4. The problem with outdated training. Most coding agents are trained on data that's a year or two out of date. They'll happily use APIs that no longer exist, are code patterns that everyone have abandoned ages ago. You've probably seen stuff. like old React lifecycle methods, non-existent library functions, deprecated Next, JS APIs, NPM commands for libraries that moved to new versions. You can't rely on training alone. You need to bring your own context, real docs, real code, real configs. Some ways to close the gap. Feed in the library

dock, for example via MCPs like Context 7. Let the agent read actual code files and dependencies. pointed to read node underscore modules, you'd be surprised how often it would help fixing the API. Add custom instructions to agents. MD telling the model to avoid the repeating problematic pattern it is trying to use. When the model knows what's actually there, it stops hallucinating. Five, layers of context. A good coding agent doesn't just read your prompt, it reads the whole situation. You can think of context in three layers, static context project structure, file layout, types,

configs, build commands, dependencies. Dynamic context the current task, open file, error messages, test results, runtime logs, external context docs, SDK references, change logs, or snippets from the web when needed. Combine all three, and the agent starts acting like someone who's actually onboarded to your codebase, not a random freelancer guessing from memory. 6. Examples from the real world. I'm currently building source wizard, coding agent for automating integrations. When I started automating the work OSoth kit integration, here's then an exhausting list of the problems that the model generated for me. Started using the deprecated and APIs. Mixed front-end and back-end logic, like using on-server side components. Generated incorrect environment variables, installed the wrong

package. It goes without saying that the model also shuffled through all of the package managers, sometimes NPM, sometimes PNPM, whatever was most interesting for the model at a time. After I've added constraints, directly specified what the agent should use, supplied with the latest API the model started generating the integration code consistently. The difference isn't in the model, it's in what it sees. Seven, context is the new interface. Most people still think about coding agents like chatbots. Give a prompt, get an answer. But for real engineering work, the prompt is just one piece. The real magic happens in the context, the files, types, commands, and feedback loops the agent can access. That's what makes it useful. In the future,

we won't just talk to coding agents. We'll wire them into our build systems. They'll understand our repos, know our tools, and follow the same rules as every other part of the stack. Conclusion, coding agents fail not because they're dumb, but because they're working blind. If you want them to be them structure, constraints that define how they should code. Build abstractions that show how your project actually runs. Up-to-date context so they stop using old patterns. The better the context, the better the code. You don't just drop a new engineer into your repo and say, figure it out. You onboard them. Coding agents are the same. Thank you for listening to this Hackernoon story, read by artificial intelligence. Visit hackernoon.com to read, write, learn, and publish.