The Complete Guide to AI Agent Architectures: ReAct, CoT, and Tool Use

What Are AI Agents?

An AI agent is an LLM-powered system that can reason about tasks, make decisions, and take actions — including calling external tools, APIs, and databases. Unlike a simple chatbot that generates text, an agent operates in a loop: observe → think → act → observe the result → think again.

The difference between a chatbot and an agent is like the difference between a calculator and a programmer. The calculator gives you an answer; the programmer breaks down your problem, writes code, tests it, debugs failures, and iterates until it works.

The ReAct Framework

ReAct (Reasoning + Acting) is the foundational agent architecture. It interleaves reasoning traces with tool use actions in a loop:

System: You are a research agent with access to these tools:
- search(query) → Returns web search results
- calculator(expression) → Returns calculation result
- lookup(term) → Returns encyclopedia entry

Use this format for each step:
Thought: [your reasoning about what to do next]
Action: [tool_name(arguments)]
Observation: [result from the tool - WAIT for this before continuing]
... (repeat Thought/Action/Observation until you have the answer)
Final Answer: [your complete answer]

Question: What is the GDP per capita of the country with the tallest building in the world?

The model would then reason through the steps: search for the tallest building → identify the country (UAE) → search for UAE GDP per capita → calculate if needed → provide the final answer.

Tool Use / Function Calling

Modern APIs (OpenAI, Anthropic, Google) provide native function calling that's more reliable than text-based ReAct:

const tools = [
  {
    type: "function",
    function: {
      name: "get_weather",
      description: "Get current weather for a location",
      parameters: {
        type: "object",
        properties: {
          location: { type: "string", description: "City name" },
          units: { type: "string", enum: ["celsius", "fahrenheit"] }
        },
        required: ["location"]
      }
    }
  },
  {
    type: "function", 
    function: {
      name: "search_products",
      description: "Search the product catalogue",
      parameters: {
        type: "object",
        properties: {
          query: { type: "string" },
          category: { type: "string" },
          max_price: { type: "number" }
        },
        required: ["query"]
      }
    }
  }
];

Key advantages over text-based tool use: structured JSON output, no parsing ambiguity, built-in validation, and parallel function calling support.

Agent Architectures Compared

Architecture	Complexity	Best For	Limitation
Single-Turn Tool Use	Low	Simple lookups, API calls	No iterative reasoning
ReAct Loop	Medium	Research, multi-step tasks	Can get stuck in loops
Plan-and-Execute	Medium	Complex workflows with clear steps	Plan may be wrong; costly to re-plan
Multi-Agent	High	Complex problems with specialised domains	Coordination overhead; harder to debug
Reflexion	High	Tasks requiring self-improvement	High token cost from reflection loops

Plan-and-Execute Architecture

Instead of reasoning one step at a time, the agent creates a complete plan first, then executes it:

System: You are a planning agent. Break the user's task into a numbered plan of 
concrete steps. Each step should specify which tool to use and what inputs to provide.

Available tools: {tool_descriptions}

After creating the plan:
1. Execute each step in order
2. After each step, verify the result matches expectations
3. If a step fails, revise the remaining plan
4. When all steps are complete, synthesise the final answer

Task: {user_task}

Plan-and-Execute is better than ReAct for tasks with clear sequential dependencies — like data pipelines, deployment workflows, or multi-step form processing. ReAct is better for exploratory tasks where the next step depends on what you discover.

Multi-Agent Systems

For complex problems, multiple specialised agents can collaborate. Common patterns:

Supervisor Pattern

Supervisor Agent: Routes tasks to specialist agents
├── Research Agent: Searches and summarises information
├── Code Agent: Writes and reviews code
├── Analysis Agent: Processes data and creates visualisations
└── Writing Agent: Produces final documentation

Debate Pattern

Agent A (Defender): Argues for the proposed solution
Agent B (Critic): Identifies flaws and risks
Agent C (Judge): Evaluates both positions and decides

Pipeline Pattern

Agent 1 (Research) → Agent 2 (Analysis) → Agent 3 (Drafting) → Agent 4 (Review)

Building Reliable Agents

Agents in production need safeguards that demonstrations and tutorials skip:

• Maximum iterations — Always cap the agent loop (e.g., max 10 steps). Unbounded agents waste tokens and can loop forever
• Tool call validation — Validate tool inputs before execution. A hallucinated SQL query shouldn't hit your production database
• Fallback behaviour — When the agent can't solve a problem in N steps, escalate to a human rather than continuing
• Structured logging — Log every thought, action, and observation. Agent debugging without logs is nearly impossible
• Cost controls — Set per-request token budgets. A rogue agent can burn through your API quota in minutes
• Sandboxing — If the agent executes code, run it in a sandboxed environment (Docker, Firecracker, E2B)

Prompt Engineering for Agents

Agent prompts require special considerations beyond standard prompt engineering:

1. Tool descriptions are prompts — The quality of your function descriptions directly impacts how well the agent uses them. Invest time here
2. Few-shot tool use examples — Show the agent how to use tools correctly with 2-3 examples in the system prompt
3. Error handling instructions — Tell the agent what to do when a tool returns an error. Otherwise it often retries the same failing call
4. Grounding instructions — "Base your answer only on tool results, not your training data" prevents hallucination
5. Completion criteria — Explicitly define when the agent should stop. "Stop when you have answered the user's question with verified data"

How AI Prompt Architect Helps

Building agents starts with building excellent prompts. AI Prompt Architect's Generate workflow can scaffold agent system prompts with tool descriptions, ReAct formatting, and safety constraints built in. The Analyse workflow evaluates your agent prompts for common failure modes: missing tool descriptions, unbounded loops, and unclear completion criteria. Use it to go from a prototype agent to a production-grade one in minutes instead of days.