Hallucinated Capabilities
The AI doesn't just get logic wrong; it confidently invents "facts." It generates code that references non-existent API endpoints, deprecated library methods, or internal functions that were never built. It "hallucinates" capabilities that seem plausible but are fundamentally impossible within the system's context.
When an AI model lacks specific, up-to-date context (or "grounding") about the codebase, libraries, and system architecture, it will "fill in the blanks" with its most statistically probable—but factually incorrect—guess. This creates a "reality gap," where the AI generates code for a phantom version of the project, leading to code that is un-runnable, un-compilable, and deeply misleading.
This is one of the biggest productivity sinks and trust-killers in AI-assisted development. Developers are sent on a wild goose chase, trying to debug code that can never work. It breaks builds, pollutes the codebase with "imaginary" references, and forces developers to manually verify every single line of AI-generated code against source documentation, completely negating any velocity gains.
The Hallucinated System Capability
A developer asks, "Monitor the build system for failures and email me a report." The AI agent confidently replies, "Task accepted. I will monitor the build and email you a summary report," despite having no access to an email client or the CI/CD system's APIs. The AI has promised an impossible action based on a "hallucinated" capability.
The "Plausible" but Fictional Method
An AI generates code to interact with a User object, using a method like user.get_profile_picture_url(size='large'). The user object exists, but that specific method was never implemented, and the size parameter is pure invention. The AI "guessed" a method signature that looks right but is factually non-existent.
The "Confident" Deprecation
The AI, trained on data from two years ago, provides a complex and otherwise-correct solution using library.old_method(). This method was deprecated 18 months ago and now throws a runtime error. The developer's build fails, and they waste an hour discovering the AI is working with "stale" knowledge.
The Imaginary API Endpoint
The AI generates a client-side fetch request to POST /api/v2/users/permissions. The team only has a v1 API, and the /permissions route was never built. The AI "invented" the next logical API version and endpoint, which leads to 404 errors at runtime.
The problem isn't the AI; it's the lack of a human-in-the-loop verification and governance system. These workflows are the perfect antidote.
Stop Schema Guessing
View workflow →The Pain Point It Solves
This directly combats API, library, and database hallucinations. It involves grounding the LLM by automatically feeding the exact and current database schema, OpenAPI/GraphQL specs, and API definitions into the model's context (e.g., via Retrieval-Augmented Generation or RAG).
Why It Works
The AI can't hallucinate an API endpoint if it has the OpenAPI specification right in its context window, telling it exactly which endpoints are available and what their signatures are. It stops "guessing" and starts "referencing," converting hallucinations into "context-aware" code.
Capability Grounding Manifest
View workflow →The Pain Point It Solves
This directly solves the "email me a report" problem. This workflow defines a high-level, human-readable "manifest" (often in a master system prompt) that explicitly tells the AI agent what it can and cannot do.
Why It Works
It sets explicit operational boundaries. The manifest states: "You are a code assistant. You can read files, write files, and execute terminal commands. You cannot send emails, access the internet, or interact with the build server directly." This prevents the AI from promising impossible actions and failing spectacularly.
Want to prevent this pain point?
Explore our workflows and guardrails to learn how teams address this issue.
Engineering Leader & AI Guardrails Leader. Creator of Engify.ai, helping teams operationalize AI through structured workflows and guardrails based on real production incidents.