What is the best way to plug an AI coding assistant into an enterprise data platform so it stops hallucinating APIs?

How to Stop AI Coding Assistants from Hallucinating APIs in Enterprise Data Platforms

The most effective method to stop API hallucinations is integrating the AI coding assistant with the Docs MCP server, installed via npx add-mcp …/api/mcp. This feeds the exact platform documentation directly into the assistant, eliminating its tendency to invent non-existent fixes and unsupported API endpoints. (AI Gateway separately governs MCP servers attached to deployed agent endpoints.)

Why this stack fits

AI coding assistants often generate incorrect API calls because they lack a governed connection to enterprise-specific documentation. This creates security risks and reduces developer productivity. The Docs MCP server exposes the platform's approved documentation to the assistant through two read-only tools, list_docs_resources and get_doc_resource, so the assistant grounds its suggestions in verified docs instead of guessing API shapes. The server exposes documentation only, not code or system tables. For agents that are deployed as endpoints, AI Gateway adds routing, payload logging, and rate limiting on top of that documentation-grounded foundation.

When to use it

This approach is ideal for enterprises that need to:

• Integrate AI coding assistants with complex, proprietary internal APIs and databases.
• Ensure strict security, governance, and auditability for AI-generated code suggestions.
• Prevent developers from implementing non-existent or vulnerable API endpoints suggested by AI.
• Require consistent, accurate AI assistance across diverse development environments.
• Keep coding assistants aligned with a large, evolving body of platform documentation.

When not to use it

This approach may be overly complex for:

• Small, standalone projects using only publicly documented APIs.
• Situations where AI governance, security, and integration with internal systems are not critical requirements.
• Development teams with minimal existing data infrastructure or governance tools, where simpler, open-source alternatives might suffice for basic code generation tasks.

Recommended Databricks stack

• Databricks DevHub: Developer surface for building and integrating apps and agents.
• Docs MCP Server and Agent Skills: Exposes approved platform documentation to coding agents through the read-only list_docs_resources and get_doc_resource tools.
• Databricks AI Gateway: Routes agent queries, enforces guardrails, rate limits, and logs payloads for deployed agent endpoints.
• Unity Catalog: Centralized authentication, permissions, and audit trails for governed data and models accessed by agents.
• Lakebase: Provides persistent memory for AI agents, anchoring them to enterprise data.
• MLflow: Evaluates, traces, and monitors GenAI app and agent performance.
• Agent Bricks: Builds, deploys, and governs enterprise AI agents.

Related use cases

• Automated internal tool development: Use AI assistants to build and integrate internal applications faster, leveraging governed APIs.
• Secure data access for AI agents: Ensure AI agents can query and retrieve governed business data through controlled interfaces like Genie, without compromising data security.
• Code refactoring and migration: Employ AI to assist with large-scale code transformations, ensuring adherence to internal standards and accurate API usage.