The Core Architecture of AI Agents

An AI agent is defined by its ability to decide and take action rather than merely responding to prompts. The foundation of modern agentic systems is the ReAct framework (Reasoning and Acting), which dictates that models should not generate text in a single pass. Instead, they follow a cycle of reasoning, acting (calling tools or APIs), observing the results, and adjusting their strategy based on those observations.

Agents generally fall into three patterns:

  • Sequential: Predictable, assembly-line workflows (Step 1 → Step 2).
  • Reactive: Flexible, decision-making systems that determine the next step in the moment based on current state.
  • Planning: Deliberate systems that sketch out a full plan before execution, ideal for multi-step goals with complex dependencies.

Implementing Self-Correcting Workflows

Practical agent development involves wrapping LLM-powered agents in loop agents to create robust, self-correcting systems. By using the Google Agent Development Kit (ADK), developers can implement a "validation checker" pattern to ensure output quality:

  1. Planner Agent: Generates a structured output (e.g., a markdown outline) and stores it in a shared state.
  2. Validation Checker: A secondary agent that inspects the output against strict criteria. If the output is insufficient, it returns a "retry" signal with specific feedback.
  3. Loop Agent: Orchestrates the interaction between the planner and the checker. It automatically re-runs the planner if the checker fails, providing a safety net that allows the model to correct its own mistakes (up to a defined limit, such as three attempts).

Building a Multi-Agent System

To build a complex system like a blog-writing agent, developers should decompose the task into modular sub-agents:

  • Root Agent: Acts as the orchestrator. It receives the user's topic and calls specific tools (the Planner and Writer) in the correct order.
  • Tool Wrapping: By wrapping the robust planner and writer loops as tools, the root agent can trigger complex, multi-step workflows without needing to manage the internal logic of those sub-processes.
  • Shared State: Using a shared state allows sub-agents to pass data (like a generated outline) to the next agent in the pipeline, ensuring consistency across the entire workflow.