What Are AI Agents?

AI agents are systems that can perceive their environment, make decisions, and take actions to achieve goals. They go beyond simple prompt-response interactions.

Pattern 1: ReAct (Reason + Act)

The model alternates between reasoning about what to do and executing actions. Best for tool-using applications.

  • Think → Act → Observe → Think → Act → ...
  • Simple, effective, widely used.

Pattern 2: Plan-and-Execute

First create a complete plan, then execute each step. Better for complex multi-step tasks.

  • Plan: Break task into ordered subtasks.
  • Execute: Complete each subtask sequentially.
  • Re-plan: Adjust the plan based on intermediate results.

Pattern 3: Multi-Agent Collaboration

Multiple specialized agents work together, each handling a different aspect of the problem.

  • Orchestrator: Coordinates other agents.
  • Specialists: Coding agent, research agent, review agent.
  • Communication via shared state or message passing.

Pattern 4: Reflection / Self-Critique

The agent reviews its own output and iterates to improve quality.

  • Generate → Critique → Revise → Critique → Final output.

Pattern 5: Human-in-the-Loop

Agent proposes actions but requires human approval for critical decisions.

Choosing a Pattern

  • Simple tool use: ReAct
  • Complex workflows: Plan-and-Execute
  • Large projects: Multi-Agent
  • High-stakes output: Reflection + Human-in-the-Loop

Advanced Agent Patterns

  • Reflection Pattern: The agent reviews its own output and iterates. Improves quality by 20-40% on complex tasks but doubles latency.
  • Planning Pattern: Break complex goals into subtasks, execute sequentially. Use for multi-step workflows like research or code generation.
  • Multi-Agent Collaboration: Specialized agents (researcher, coder, reviewer) collaborate via a coordinator. Best for complex projects.
  • Tool-Augmented Agents: Agents that dynamically discover and use tools via MCP or function calling. More flexible than hardcoded workflows.

Production Considerations

  • Error recovery: Agents fail. Build retry logic, fallback paths, and circuit breakers.
  • Cost control: Each agent step costs money. Set max iteration limits and token budgets.
  • Observability: Log every decision, tool call, and intermediate result. Essential for debugging.
  • Human-in-the-loop: For high-stakes decisions, require human approval before executing actions.