Agentic Architecture

The evolution of AI has moved from rule-based systems to machine learning—and now into a new paradigm: agentic architecture. Instead of stateless tools, we're building autonomous, goal-driven agents that reason, remember, and act.

Modern TypeScript frameworks now make this practical, with strong typing, modular design, and composable tooling.

What Is Agentic Architecture?

Agentic systems are AI programs that:

• Perceive their environment
• Reason over goals
• Use external tools
• Maintain memory
• Act over time, not just on demand

Where a chatbot answers, an agent decides. Where a tool executes, an agent plans.

Three key enablers make this accessible:

1. LLM capabilities: Models like GPT-4o and Claude can reason, plan, and call functions.
2. TS-first SDKs: Frameworks like LangChain.js, Vercel AI SDK, Mastra, and LlamaIndex.ts are built for TS.
3. Infra support: Vector DBs (pgvector, Redis), streaming backends, and orchestration tools are battle-tested.

Anatomy of an Agentic System in TypeScript

Here's a breakdown using real-world frameworks. Each component addresses a core agentic capability:

• Reasoning Engine handles planning and decision-making.
• Memory Layer enables retrieval of past context.
• Planning + Execution integrates tools and memory in a goal-directed loop.
• Tool Interface allows structured function calls.
• Streaming UI ensures responsive, real-time interaction.
• Feedback + Reflection supports learning from outcomes.

Reasoning Engine (LangChain.js)

import { initializeAgentExecutorWithOptions } from 'langchain/agents';
 
const executor = await initializeAgentExecutorWithOptions(
  [searchTool, calculatorTool],
  chatOpenAI,
  { agentType: "openai-functions", verbose: true }
);
 
const result = await executor.run("What's the weather in Berlin and the square root of 2024?");

Memory Layer (Supabase + pgvector)

const { data } = await supabase.rpc('match_documents', {
  embedding: await getEmbedding(input),
  match_threshold: 0.78,
  match_count: 5,
});

With LangChain.js:

const retriever = new SupabaseVectorStore(...);
const chain = RetrievalQAChain.fromLLM(chatOpenAI, retriever);

Planning + Execution (Mastra)

import { Agent } from 'mastra';
 
const agent = new Agent({
  tools: [search, writeToNotion],
  memory: new VectorMemory({ db: pgvector }),
});
 
await agent.run("Plan a 7-day trip to Japan with vegan food stops");

Tool Interface

LangChain.js:

new DynamicTool({
  name: "searchDocs",
  description: "Search internal documents",
  func: async (input) => queryDocs(input),
})

Agentic.so:

import { defineTools } from '@agentic/sdk';
 
export const tools = defineTools({
  getStockPrice: async (ticker) => fetchPrice(ticker),
});

Streaming UI (Vercel AI SDK)

Client:

'use client';
import { useChat } from 'ai/react';

const { messages, input, handleInputChange, handleSubmit } = useChat({ api: '/api/agent' });

API route:

export const runtime = 'edge';
import { OpenAIStream, StreamingTextResponse } from 'ai';
 
export async function POST(req: Request) {
  const { messages } = await req.json();
  const response = await openai.chat.completions.create({...});
  return new StreamingTextResponse(OpenAIStream(response));
}

Feedback + Reflection

const reflection = await openai.chat.completions.create({
  messages: [
    { role: 'system', content: 'Reflect on this failed attempt...' },
    { role: 'user', content: logOfFailedPlan }
  ],
});

LangChain.js and Mastra support retries and self-reflection hooks.

Use Cases

• Developer agents
• Support agents with memory and ticket history
• Autonomous analysts and growth bots
• Scientific research assistants
• Legal research assistants
• AI executive assistants
• Financial advisors
• Healthcare copilots
• E-commerce agents

Key Challenges

Final Thoughts

Agentic architecture turns LLMs into collaborative co-pilots. TypeScript developers now have everything needed—mature SDKs, runtime support, and frontend integration—to build intelligent agents that are production-ready.