Modern AI Agent Architecture

Fundamental Patterns

Building robust AI agents requires understanding the architectural patterns that have proven effective in production.

ReAct: Reasoning + Acting

The ReAct pattern combines reasoning with action in an iterative loop:

async function reactLoop(agent: Agent, task: string) {
  let context = { task, history: [] };
 
  while (!context.done) {
    const thought = await agent.think(context);
    const action = await agent.selectAction(thought);
    const result = await agent.execute(action);
 
    context.history.push({ thought, action, result });
    context.done = await agent.shouldStop(context);
  }
 
  return agent.synthesize(context);
}

Plan and Execute

This pattern separates planning from execution:

1. Planning: The agent generates a complete plan
2. Execution: Each step is executed sequentially
3. Re-planning: If something fails, the agent re-plans

Multi-Agent Systems

Multi-agent systems distribute complexity among specialized agents:

• Orchestrator: Coordinates the workflow
• Researcher: Searches and synthesizes information
• Coder: Writes and reviews code
• Reviewer: Validates output quality

Memory and State

Memory management is crucial for effective agents:

Short-term memory

The current conversation context and recent actions.

Long-term memory

Persistent knowledge stored in vector databases or knowledge graphs.

Conclusion

Choosing the right architecture depends on the use case, task complexity, and reliability requirements. There is no one-size-fits-all solution, but understanding these patterns enables informed decisions.