Understanding AI Agents: Architecture Patterns That Work

Every major tech company is shipping AI agents in 2026. But behind the marketing buzzwords, what actually makes an AI agent work? And more importantly — what architecture patterns separate the demos from production systems?

This guide breaks down the four dominant agent architecture patterns, with code examples you can adapt to your own projects.

What Is an AI Agent, Really?

An AI agent is software that uses a language model to decide what actions to take, execute those actions using tools, and iterate based on the results. Unlike a simple chatbot that responds to prompts, an agent has a loop:

The key distinction: a chatbot responds once; an agent loops until the job is done.

Three properties define a true agent:

1. Autonomy — it decides which tools to use and in what order
2. Tool use — it can call APIs, run code, read files, query databases
3. Memory — it maintains context across its action loop

Pattern 1: The ReAct Loop

The simplest and most widely-used pattern is ReAct (Reasoning + Acting). The model alternates between reasoning about what to do and taking an action.

class ReActAgent:
    def __init__(self, llm, tools):
        self.llm = llm
        self.tools = {t.name: t for t in tools}
        self.max_steps = 10

    def run(self, task: str) -> str:
        messages = [{"role": "user", "content": task}]
        
        for step in range(self.max_steps):
            # THINK: Ask the LLM what to do
            response = self.llm.chat(messages, tools=self.tools)
            
            if response.is_final_answer:
                return response.content
            
            # ACT: Execute the chosen tool
            tool_name = response.tool_call.name
            tool_args = response.tool_call.arguments
            result = self.tools[tool_name].execute(**tool_args)
            
            # OBSERVE: Feed result back
            messages.append({"role": "tool", "content": result})
        
        return "Max steps reached without resolution."

When to Use ReAct

• Single-purpose tasks: "Find the bug in this function"
• Linear workflows where each step informs the next
• Prototyping — it's the fastest pattern to implement

Limitations

ReAct struggles with tasks that require parallel work or planning ahead. It's purely reactive — the model only thinks one step at a time.

Pattern 2: Plan-and-Execute

This pattern separates planning from execution. A planner model creates a full plan upfront, then an executor works through each step.

class PlanAndExecuteAgent:
    def __init__(self, planner_llm, executor_llm, tools):
        self.planner = planner_llm
        self.executor = executor_llm
        self.tools = tools

    def run(self, task: str) -> str:
        # Phase 1: Create the plan
        plan = self.planner.chat([
            {"role": "system", "content": "Create a step-by-step plan."},
            {"role": "user", "content": task}
        ])
        
        steps = parse_plan(plan.content)
        results = []
        
        # Phase 2: Execute each step
        for step in steps:
            result = self.executor.chat([
                {"role": "system", "content": f"Execute: {step}"},
                {"role": "user", "content": f"Prior results: {results}"}
            ], tools=self.tools)
            results.append(result)
        
        # Phase 3: Synthesize
        return self.planner.summarize(task, results)

When to Use Plan-and-Execute

• Complex multi-step tasks: "Refactor this module and update all tests"
• When you need predictability — the user can review the plan before execution
• Tasks where total cost matters (the plan constrains token usage)

Pattern 3: Multi-Agent Orchestration

For complex systems, a single agent isn't enough. Multi-agent orchestration uses specialized agents coordinated by a router or orchestrator.

class MultiAgentOrchestrator:
    def __init__(self, agents: dict, router_llm):
        self.agents = agents  # {"coder": CoderAgent, "reviewer": ReviewAgent, ...}
        self.router = router_llm

    def run(self, task: str) -> str:
        # Router decides which agent handles the task
        routing = self.router.chat([
            {"role": "system", "content": self.routing_prompt()},
            {"role": "user", "content": task}
        ])
        
        agent_name = routing.selected_agent
        agent = self.agents[agent_name]
        
        # Delegate to the specialist
        result = agent.run(task)
        
        # Optionally pass to another agent for review
        if routing.needs_review:
            review = self.agents["reviewer"].run(
                f"Review this output:\n{result}"
            )
            return f"{result}\n\nReview: {review}"
        
        return result

Real-World Example: Code Review Pipeline

A production code review system might use three agents:

1. Security Agent — scans for vulnerabilities, credential leaks, injection risks
2. Logic Agent — checks for bugs, race conditions, edge cases
3. Style Agent — enforces conventions, naming patterns, documentation

Each agent has different system prompts, different tools, and different evaluation criteria. The orchestrator merges their feedback into a single review.

Pattern 4: Reflexion (Self-Correcting Agents)

The most advanced pattern adds self-evaluation. After producing output, the agent critiques its own work and iterates.

class ReflexionAgent:
    def __init__(self, actor_llm, critic_llm, tools):
        self.actor = actor_llm
        self.critic = critic_llm
        self.tools = tools
        self.max_reflections = 3

    def run(self, task: str) -> str:
        attempt = self.actor.chat(
            [{"role": "user", "content": task}],
            tools=self.tools
        )
        
        for i in range(self.max_reflections):
            # Self-evaluate
            critique = self.critic.chat([{
                "role": "user",
                "content": f"Task: {task}\nAttempt:\n{attempt}\n\n"
                           f"Is this correct and complete? "
                           f"If not, what needs to change?"
            }])
            
            if critique.is_satisfactory:
                return attempt.content
            
            # Retry with feedback
            attempt = self.actor.chat([
                {"role": "user", "content": task},
                {"role": "assistant", "content": attempt.content},
                {"role": "user", "content": f"Feedback: {critique.content}"}
            ], tools=self.tools)
        
        return attempt.content

When to Use Reflexion

• High-stakes outputs: code generation, data analysis, report writing
• When you can define clear evaluation criteria
• When the cost of iteration is lower than the cost of errors

Choosing the Right Pattern

Production Considerations

1. Guard Rails Are Non-Negotiable

Every production agent needs:

• Token budgets — cap the maximum loop iterations and total tokens
• Tool allowlists — restrict which tools the agent can call
• Output validation — schema validation on structured outputs
• Human-in-the-loop — escalation paths for uncertain decisions

2. Observability

You cannot debug agents with console.log. You need:

• Trace IDs across every LLM call and tool invocation
• Step-by-step logging of the agent's reasoning
• Cost tracking per task (tokens consumed, API calls made)
• Latency breakdowns showing where time is spent

3. Evaluation

Before deploying, build an eval suite:

eval_cases = [
    {"input": "Find all Python files with SQL injection risks",
     "expected_tools": ["grep", "file_read"],
     "expected_output_contains": ["parameterized", "sanitize"]},
]

for case in eval_cases:
    result = agent.run(case["input"])
    assert all(t in result.tools_used for t in case["expected_tools"])
    assert all(k in result.output for k in case["expected_output_contains"])

What's Next

The agent landscape is moving fast. Key trends to watch:

• MCP (Model Context Protocol) — standardizing how agents connect to tools
• Agent-to-agent communication — agents delegating to other agents across organizational boundaries
• Persistent memory — agents that learn from past interactions and improve over time
• Formal verification — mathematical guarantees about agent behavior

The pattern you choose matters less than the guardrails you put around it. Start with ReAct, graduate to multi-agent when your use case demands it, and always instrument everything.

Published by the TechAI Explained Team.