Agent Orchestration Patterns: ReAct, Plan-and-Execute, and Beyond

The Agent Landscape in 2026

AI agents have moved from research demos to production systems. But building reliable agents requires choosing the right orchestration pattern for your use case. The wrong pattern leads to infinite loops, wasted tokens, and unpredictable behavior.

Pattern 1: ReAct (Reasoning + Acting)

The simplest and most common pattern. The agent thinks about what to do, takes an action (calls a tool), observes the result, and repeats until the task is done.

import Vincony from "vincony";

const client = new Vincony({ apiKey: "YOUR_API_KEY" });

// ReAct agent with tools
const agent = await client.agents.create({
  pattern: "react",
  model: "gpt-4.1",
  tools: [
    { name: "web_search", description: "Search the web for information" },
    { name: "calculator", description: "Perform mathematical calculations" },
    { name: "code_exec", description: "Execute Python code in a sandbox" }
  ],
  max_iterations: 10,
  stop_conditions: ["task_complete", "max_iterations", "confidence_threshold"]
});

const result = await agent.run(
  "What's the current market cap of the top 5 AI companies? Calculate their combined value."
);

console.log(result.answer);
console.log(`Completed in ${result.iterations} steps, ${result.tokens_used} tokens`);

Pattern 2: Plan-and-Execute

Separates planning from execution. A planner model creates a step-by-step plan, then an executor model carries out each step. Better for complex, multi-step tasks where upfront planning prevents wasted work.

// Plan-and-Execute agent
const agent = await client.agents.create({
  pattern: "plan_and_execute",
  planner: {
    model: "claude-sonnet-4",    // Strong reasoning for planning
    replanning: true              // Re-plan if a step fails
  },
  executor: {
    model: "gpt-4.1-mini",       // Fast model for execution
    tools: ["web_search", "file_read", "file_write", "code_exec"]
  },
  max_replans: 3
});

const result = await agent.run(
  "Analyze our competitor's pricing pages and create a comparison spreadsheet"
);

// Shows the plan and execution trace
result.plan.steps.forEach((step, i) => {
  console.log(`Step ${i + 1}: ${step.description} — ${step.status}`);
});

Pattern 3: Tree of Thought

For problems requiring exploration of multiple solution paths. The agent generates several approaches, evaluates each, and pursues the most promising one. More expensive but dramatically better for complex reasoning tasks.

Pattern 4: Reflection

The agent generates an output, then critiques its own work and iterates. Simple but effective for tasks where self-review catches errors — like code generation, writing, and analysis.

Choosing the Right Pattern

ReAct for simple tool-use tasks. Plan-and-Execute for multi-step workflows. Tree of Thought for complex reasoning. Reflection for quality-critical outputs. Vincony supports all patterns with built-in guardrails and cost limits.

Pricing

Agent runs are billed per model call at standard credit rates. Plan-and-Execute typically uses 2-5x the tokens of a single call. Tree of Thought uses 3-10x. Set budget limits per agent run to prevent runaway costs.