The Deska blog
The Agent Invented an API: Catching Hallucinated Code Early
Learn why AI agents create non-existent libraries and how to catch hallucinated code using local execution, terminal feedback, and side-by-side verification.
· 11 min read
AI agents are transforming the speed of software development, but they bring a specific technical risk known as hallucinated code. This phenomenon occurs when a Large Language Model (LLM) generates calls to non-existent libraries, imaginary API endpoints, or deprecated parameters that appear syntactically correct but fail during execution. Catching these errors early is the difference between a productive automated workflow and hours spent debugging invisible ghosts in your codebase.
Why Agents Invent APIs
The fundamental architecture of an AI agent relies on probabilistic prediction. When you ask an agent to integrate a specific service, it predicts the next most likely tokens based on its training data. If that training data is stale or includes similar but distinct libraries, the agent might blend them together.
Hallucinations often creep in via three specific patterns. First, the agent might guess an administrative endpoint based on common REST conventions, such as assuming /api/v1/user/delete exists when the actual path is /api/v1/users/:id. Second, it may invent "convenience methods" for popular libraries that simply do not exist in the current version of the package. Third, agents often struggle with internal private APIs that were never part of their training set, leading them to guess names based on folder structures.
The Cost of Delayed Detection
If hallucinated code makes it past the initial generation phase, it becomes significantly harder to fix. A developer might review the code and find it logically sound: the loops are correct, the variables are named well, and the error handling looks robust. The flaw is hidden in the external dependency.
When this code is committed without execution, it breaks the CI/CD pipeline. Even worse, if the hallucination involves a security parameter that doesn't actually exist, the code might run but fail to apply the intended protections. This creates a false sense of security where the developer believes a feature is implemented when it is actually a non-functional shell.
Strategies for Verification
To mitigate these risks, developers must move away from "blind copy-pasting" toward an interactive verification loop. The following strategies help identify imaginary code before it reaches your main branch.
- Immediate Runtime Execution: Never trust a code block until it has been run in a real environment. If the agent provides a script, execute it immediately in a terminal.
- Strict Type Checking: Using languages with strong typing or tools like TypeScript can highlight non-existent methods before you even run the code.
- Documentation Cross Referencing: Keep a browser window open with the official API documentation. If an agent suggests a method name that looks unfamiliar, a quick search can confirm its existence.
- Sandbox Isolation: Use an environment where you can throw away the changes if the agent begins to drift into a hallucination loop.
How Managed Workspaces Help
Specific tools are designed to narrow the gap between code generation and execution. For example, Deska provides an infinite canvas where you can keep your agent and your execution environment in the same visual field. This layout is crucial because it allows you to see the agent generating code in one panel while watching the logs of a terminal in another.
By running agents like Claude Code or OpenCode side by side as interactive panels, you can catch a hallucination the moment the terminal spits out a 404 or a "function not found" error. This local-first approach ensures that your real files and sessions stay on your machine, allowing the agent to read your actual file system rather than guessing what your project structure looks like.
Comparing Agent Architectures
Different agents handle the risk of hallucination in various ways. It is helpful to understand how they approach the problem of accuracy.
| Agent Type | Strategy | Strength | Weakness |
|---|---|---|---|
| CLI Agents | Direct Shell Access | Can verify commands immediately | Might execute destructive code |
| Chat Assistants | Context-Based | Good at explaining logic | Often hallucinate imports |
| IDE Plugins | Autocomplete Style | High speed during typing | Limited context of entire project |
Agents like Codex CLI and Claude Code, which can be managed within panels, often perform better because they have direct access to your terminals. When an agent can run its own code and read the output, it creates a self-correcting loop. If it hallucinations an API, the error message from the terminal acts as a prompt for the agent to try a different approach.
Integrating AI into Your Workflow Safely
The goal is not to eliminate AI, but to build a workflow that assumes the AI will occasionally lie. Utilizing the Ask Deska assistant can help manage this workspace. You can use voice or chat to quickly open a new browser widget to verify an API's existence or ask the assistant to check the status of your current agent threads.
By maintaining a mobile connection via the secure relay, you can even monitor long-running agent tasks while away from your desk. If you notice a series of error messages in the terminal output on your phone, you know the agent has hit a hallucination wall and needs intervention.
FAQ
How to stop AI agents from inventing libraries?
The most effective way to stop an agent from inventing libraries is to provide it with the current documentation of the libraries you are using. You can do this by pasting the relevant API references into the chat or by using agents that have the ability to browse the web for the latest versions. Keeping your environment restricted to specific versions also helps.
Why does Claude Code hallucinate API keys?
Claude Code and similar agents do not actually know your private keys unless you provide them. They might generate placeholder strings that look like keys or suggest environment variable names that do not exist. Always manage your credentials locally and use a BYOK model where you control the API keys used for the model inference itself.
Can AI agents fix their own hallucinations?
Yes, if the agent has access to the terminal. When an agent runs code and receives an error message like AttributeError or ModuleNotFoundError, it can use that feedback to realize the library or method it suggested does not exist. This iterative process is why integrated workspaces are more effective than standalone chat windows.
Get Started with Deska
If you are ready to build a more resilient development environment, you can experiment with these agent workflows today. Deska is available as a free download for Mac, Windows, and Linux. It allows you to run multiple agents in a single, infinite workspace while keeping your code local and secure.
Experience the benefit of seeing your code, your terminal, and your AI agents all at once to catch hallucinations before they become bugs. Download Deska here and start building.