Build AI Agents in Minutes

Two main types: an AI assistant retrieval agent for chat (single data source, context-specific responses) and an advanced agent for complex processes (multiple tools). You add context (e.g. Wikipedia or SQL), connect an LLM, add a prompt, then save and publish with guardrails.

Guardrails are rule-based policies that ensure safe, accurate, and appropriate content (docs.superwise.ai): rules, ethical guidelines, and content filters. They prevent harmful or misleading outputs and can restrict topics (e.g. block “How old is Taylor?”). You combine one or more rules, test in a playground, and use them natively when deploying or via API/SDK—so the check runs before the answer is returned.

Agents that answer from a knowledge base can drift off-topic or leak sensitive content. Guardrails block or audit those cases before they reach users. Governed AI requires that you enforce safety at build time and at runtime.

RAG agents mix user input and retrieved context. Without guardrails and observability, you cannot control what gets retrieved or returned. SUPERWISE gives you rules, visibility, and audit so you trust what the agent says and does.