Listen to this Post
The evolution of AI agents ranges from simple LLMs to fully autonomous systems. Here’s a structured breakdown:
🐣 Beginner Tier (Basic LLMs)
- LLMs (GPT-4, Claude, Perplexity, Mistral) – Raw reasoning, no orchestration.
- Custom LLMs (CustomGPT, Chipp) – Single-instance AI with more control.
🐥 Intermediate Tier (Workflow Automation)
- Make.com – Triggers LLMs but lacks true agency.
- n8n – Open-source automation, deterministic workflows.
- Relevance AI – Embeddings, retrieval, and light logic.
🐓 Advanced Tier (Multi-Step Reasoning)
- LangGraph – Memory, tool calling, multi-step reasoning.
- Haystack Agents – Specialized in RAG (Retrieval-Augmented Generation).
🦅 Expert Tier (Semi-Autonomous Agents)
- Autogen – Self-feedback loops, team-based AI.
- Flowise – No-code LLM chaining, fast prototyping.
- Crew AI – Multi-agent systems with defined roles.
🔮 Experimental Tier (Future of Autonomy)
- AutoGPT – Early attempts at full autonomy.
- Twin.so – Claims to be true agentic infrastructure.
You Should Know: Practical AI Agent Implementation
- Setting Up a Basic AI Workflow with n8n
Install n8n (Node.js required) npm install n8n -g n8n start
– Access `http://localhost:5678` to configure AI triggers.
2. Running Autogen for Multi-Agent Collaboration
Install Autogen
pip install pyautogen
Sample multi-agent setup
import autogen
config_list = [{"model": "gpt-4", "api_key": "YOUR_OPENAI_KEY"}]
assistant = autogen.AssistantAgent("assistant", llm_config={"config_list": config_list})
user_proxy = autogen.UserProxyAgent("user_proxy", code_execution_config={"work_dir": "coding"})
user_proxy.initiate_chat(assistant, message="Plan a marketing strategy.")
3. Deploying a RAG System with Haystack
pip install farm-haystack
from haystack import Pipeline from haystack.document_stores import InMemoryDocumentStore from haystack.nodes import EmbeddingRetriever, PromptNode document_store = InMemoryDocumentStore() retriever = EmbeddingRetriever(document_store=document_store, embedding_model="sentence-transformers/all-mpnet-base-v2") prompt_node = PromptNode(model_name_or_path="gpt-4", api_key="YOUR_KEY") pipeline = Pipeline() pipeline.add_node(component=retriever, name="Retriever", inputs=["Query"]) pipeline.add_node(component=prompt_node, name="PromptNode", inputs=["Retriever"]) result = pipeline.run(query="Explain AI agent workflows.")
What Undercode Say
AI agents are shifting from simple chatbots to autonomous systems capable of replacing human workflows. The key is hands-on experimentation—whether using n8n for automation, Autogen for multi-agent collaboration, or Haystack for RAG. Future advancements will likely focus on self-improving agents (AutoGPT, Twin.so).
Expected Output:
- A structured AI agent workflow.
- Deployed RAG system for dynamic responses.
- Multi-agent collaboration for complex tasks.
Prediction
AI agents will soon handle end-to-end business processes with minimal human intervention, making Crew AI and Autogen critical for enterprise automation.
(Relevant URLs if needed: Autogen GitHub, Haystack Docs)
References:
Reported By: Leadgenmanthan Want – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
