The Agentic Stack

Architectural blueprints for autonomous systems.

Layer 01

The Reasoning Brain

Frontier & Specialist Models

The core intelligence layer. Modern stacks use a 'Router' pattern to send complex reasoning tasks to frontier models while offloading simple formatting or extraction to fast, small models (SLMs).

// Pattern: LLM Routing Logic
const agentBrain = async (input: string) => {
  const complexity = await analyzeComplexity(input);
  
  // Route to high-reasoning vs high-speed
  const model = complexity > 0.7 
    ? "claude-3-5-sonnet" 
    : "gpt-4o-mini";

  return await llm.generate({ model, prompt: input });
};

Layer 02

The Orchestration

State Management & Cognitive Loops

The nervous system that manages multi-step workflows. It handles the 'ReAct' loop (Reason + Act), enabling the agent to observe its own mistakes and correct them before final output.

// Pattern: ReAct Loop (LangGraph style)
const workflow = new StateGraph(AgentState)
  .addNode("think", reasoner)
  .addNode("act", toolExecutor)
  .addEdge("think", (state) => {
    return state.hasToolCall ? "act" : "__end__";
  })
  .addEdge("act", "think");

Layer 03

The Memory Vault

Context & Long-term Recall

Agents need 'Short-term' memory (conversation history) and 'Long-term' memory (learning user preferences over months). This layer manages Vector DBs and Semantic Cache.

// Pattern: Semantic Memory Retrieval
    const getContext = async (userId: string, query: string) => {
    // 1. Fetch vector context from Pinecone
    const vectorDocs = await vectorStore.search(query);
  
    // 2. Fetch user preferences from Mem0
    const userPrefs = await memory.get(userId);

    return `Prefs: ${userPrefs} \n Context: ${vectorDocs}`;
};

Layer 04

The Action Layer

Tools, Browsers & MCP

The 'Hands' of the agent. This layer uses tool-calling to interact with external APIs, execute Python code in sandboxes, or navigate websites to find information.

// Pattern: Tool Definition (JSON Schema)
const tools = [{
  type: "function",
  function: {
    name: "get_stock_price",
    description: "Fetches live price for a symbol",
    parameters: {
      type: "object",
      properties: {
        symbol: { type: "string" }
      }
    }
  }
}];

Layer 05

The Guardrails & Evals

Observability & Trust Systems

The safety net. In production, agents must be monitored for 'hallucination rates,' tool-calling accuracy, and cost spikes. This layer ensures that autonomous actions remain within corporate policy and safety boundaries.

// Pattern: LLM-as-a-Judge Evaluation
const evalAgent = async (prediction, reference) => {
  const feedback = await evaluator.check({
    input: prediction,
    criteria: "conciseness, accuracy, tone",
    reference: reference
  });

  if (feedback.score < 0.8) {
    throw new Error("Quality threshold not met");
  }
  return feedback;
};