Agentic AI System Architecture: Beyond Chatbots

For the past few years, LLM-powered applications have been dominated by the chatbot paradigm. A user asks a question, the LLM processes it and returns a response. While useful, this is a passive, synchronous interaction.

The future belongs to Agentic AI—applications where LLMs don't just chat, but actively plan, make decisions, execute tools, and correct their own errors to achieve a defined goal.

In this article, we outline the foundational modules required to transition from a chat application to an autonomous agentic system.

The Four Pillars of Agentic AI

An autonomous agent is composed of four core modules:

Planning: Breaking down a complex goal into smaller steps.

Memory: Tracking state, conversations, and past outcomes.

Tools: Interacting with external systems (databases, APIs, web browsers).

Action/Execution: The runtime execution loop that coordinates the system.

1. The Planning Module

For complex tasks (e.g. "conduct market research on competitor X"), an agent cannot output the final answer immediately. It needs a plan.

ReAct (Reason + Action): The agent writes a thought, takes an action, observes the result, and repeats.

Plan-and-Solve: The agent plans the entire sequence of steps first, then executes them sequentially, revising the plan if tool outputs present new parameters.

Self-Reflection: Analyzing its own output, identifying errors (e.g., failed API calls, syntax errors in generated code), and correcting them before responding to the user.

2. The Tool Execution Bridge

An LLM on its own is locked in its weights. Tools are its window to the world. We define tools using schema declarations that the LLM reads via function calling:

// Schema declaration for a custom DB search tool
const searchDatabaseTool = {
  name: "search_db",
  description: "Search corporate databases for active customer contracts.",
  parameters: {
    type: "object",
    properties: {
      customerName: { type: "string", description: "The name of the company" }
    },
    required: ["customerName"]
  }
};

When the LLM decides to search the database, it outputs a structured JSON object:

{
  "name": "search_db",
  "arguments": {
    "customerName": "Acme Corp"
  }
}

The execution bridge intercepts this JSON, executes the database query, and sends the raw result back to the LLM as a tool message.

3. Stateful Memory Architectures

Agents require two types of memory:

Short-term Memory: The in-flight message history of the current goal execution.

Long-term Memory (Episodic): Storing lessons, user preferences, and results of past runs in a vector database.

By retrieving past execution memories, agents learn. If an agent previously encountered an error calling a specific API endpoint, it can recall that error from its episodic memory and adjust the argument payload on its next attempt.

The Agent Loop

At its core, the agent runtime runs an autonomous event loop:

async function agentLoop(userGoal) {
  let state = { goal: userGoal, steps: [], feedback: [] };
  
  while (true) {
    const decision = await llm.decide(state);
    
    if (decision.type === "finish") {
      return decision.finalOutput;
    }
    
    if (decision.type === "tool_call") {
      const observation = await executeTool(decision.tool, decision.args);
      state.steps.push({ tool: decision.tool, observation });
    }
    
    if (decision.type === "self_correct") {
      state.feedback.push(decision.errorDetails);
    }
  }
}

This runtime loop enables autonomous agents to perform complex, multi-step actions on behalf of the user, unlocking a new class of proactive AI software.

Beyond Chatbots: The Architecture of Agentic AI Applications

The Four Pillars of Agentic AI

1. The Planning Module

2. The Tool Execution Bridge

3. Stateful Memory Architectures

The Agent Loop