Why Your AI Agent Keeps Calling the Wrong Tool (and How to Fix It)

A developer at apcore has introduced a new standard called apcore to address the problem of AI agents calling the wrong tools due to vague descriptions. The standard separates short descriptions from detailed documentation, allowing agents to access the manual only when considering a tool, which improves accuracy and autonomy. The approach aims to shift from 'prompt engineering' to more reliable software engineering for agentic systems.

It’s Friday afternoon. You’ve just deployed a sophisticated AI Agent with a suite of 50 enterprise tools. Five minutes later, the logs show a disaster: the Agent was supposed to deactivate user for a support ticket, but instead, it hallucinated and called delete user. Why? Because the text descriptions were "too similar," and the LLM felt lucky. If you’ve spent any time building Agentic systems, you know this pain. We’ve been building mission-critical automation on top of "Vibes"—fuzzy string descriptions and loose JSON objects. Let’s look at why traditional tool-calling is failing and how we can move toward a world of AI-Perceivable modules.The "Vibe-Based" Engineering Crisis On the surface, this looks fine. But as your system scales from 5 tools to 50 or 500, several critical failure points emerge:- Description Overlap: If you have remove user, delete account, and deactivate member, the LLM often picks the wrong one based on a slight nuance in the user's prompt. We are essentially trying to "Prompt Engineer" our way into reliable software. That is not engineering; that’s hope.Introducing apcore: The AI-Perceivable Standard At apcore, we believe that if a module is to be invoked by an AI, it must be AI-Perceivable. This means the module must explicitly communicate its structure, its behavior, and its constraints in a way that the AI doesn't have to "guess." Let's look at the same delete user tool implemented as an apcore module in Python: from apcore import Module, ModuleAnnotations, Context from pydantic import BaseModel, Why this is a game-changer:- Dual-Layered Intelligence: We separate the description short, for discovery from the documentation long, for detailed planning . The AI only reads the "manual" when it's actually considering using the tool. The Agent reads the ai guidance, realizes its mistake, fetches the correct UUID, and retries—autonomously. This is the path to truly resilient Agentic systems.Conclusion: Stop Prompting, Start Engineering