Your Coding Agent Is Failing — Here’s Why

AI coding assistants like GitHub Copilot, Replit Ghostwriter, and ChatGPT are revolutionizing software development. But they’re not magic and they can break. Understanding why they fail and how to fix it is key to building reliable workflows and documentation tools like PRDHub.

Lack of Context

AI agents hallucinate code when they don’t have the full picture — like calling a function that doesn’t exist. LLMs only “see” a limited number of tokens, such as a few files or code snippets. If important parts are missing, they guess — and sometimes guess wrong.

One way to reduce hallucinations is to provide well-structured documents like Product Requirements and Technical specs. This context helps clarify what the code is supposed to do. PRDHub helps by generating these artifacts — including PRDs, technical documentation, and engineering tasks — through guided conversations. While PRDHub doesn't yet integrate directly with IDEs or coding agents, users can take the generated output and feed it manually into tools like Copilot Chat or GPT-based agents to improve outcomes. We’re actively exploring tighter integrations in the future.

Vague Prompts

Telling an AI to “add login” often results in insecure or incomplete code. That’s because LLMs are pattern matchers, not mind readers. They respond with the most statistically likely solution, which may not align with your intentions. Like humans, agents make assumptions to fill in the blanks — and those assumptions may be wrong.

Improving results means being more specific. Saying “add login with email and password, no OAuth, validate credentials on the backend” leads to better code. PRDHub supports this kind of clarity by guiding users through structured flows to define product features, technical constraints, and edge cases. These details become part of a comprehensive specification that users can refer back to or feed into AI tools.

Long, Complex Tasks

Asking an AI to “build an app” can send your Coding agent into a loop. These systems often lose track of their goals, mismanage state, or produce only partial results. This happens because LLMs don’t have persistent memory or robust planning ability.

The best way to handle complexity is to break things down. Large goals should be decomposed into smaller steps, each with clear intent and scope. PRDHub does this by guiding users from high-level ideas to well-scoped tasks, ready for implementation. While PRDHub doesn’t yet automate these tasks in your development environment, it makes it easier to manage the process — and hand off each unit to the right tool or team member. Giving structure to complexity is half the battle.

Final Thought

Your coding agent will break. The question is when, not if. But if you give it precise instructions, supply the right context, break down complexity, and stay in the loop, you can turn failure into iteration — and eventually, into something useful.

That’s where tools like PRDHub come in. It doesn’t write code for you or plug directly into your IDE — at least not yet. But it helps you build the scaffolding: clear requirements, technical specs, and actionable tasks. These are the foundations of effective AI-assisted development. And when your agent inevitably stumbles, that structure is what will help you pick it back up and move forward.