Input-Generate-Output architecture

A fundamental design pattern for AI workflows and autonomous agents, where a system transforms a starting state through a computational transformation layer to reach a terminal state.

Core Components

• Input: The initial data, user query, or environmental trigger that initiates the workflow.
• Generate: The processing layer involving reasoning, computation, or transformation. This often involves prompt-chaining, llms, and the integration of tools.
• Output: The resulting artifact, response, or actionable product produced by the system.

Implementations

• Google Opal (Experiment): A no-code implementation of this architecture from google-labs.
  • Allows users to describe, create, and share AI mini-apps using natural language.
  • Leverages the architecture by chaining together prompts, models, and tools.

Backlinks

• 2026 04 14 No code AI development using Opal