🤖 How to Build Agentic AI Systems from Scratch
Learn how to build autonomous, intelligent AI agents that plan, reason, and act across tools and memory systems. This step-by-step guide covers components, architecture, tech stack, and a live example.
🧠 What is an Agentic AI System?
An Agentic AI system accepts a goal, plans actions, uses tools, stores memory, and adapts decisions autonomously. It can operate across APIs, documents, websites, or databases to solve complex real-world problems.
🪜 Step-by-Step Guide
✅ Step 1: Define the Agent’s Purpose
Decide what the agent should achieve and where it operates (web, documents, APIs).
Example: A Document Research Agent that answers user questions from internal files.
✅ Step 2: Design Core Capabilities
- Planner: Breaks down tasks
- Memory: Stores intermediate steps & context
- Reasoner: Chooses actions based on context
- Executor: Calls tools/APIs
- LLM Interface: Natural language understanding & generation
✅ Step 3: Choose the Technical Stack
- LLMs: GPT-4, Claude, Mistral, LLaMA
- Frameworks: LangChain, LangGraph, CrewAI
- Memory: FAISS, Redis, ChromaDB
- Planning: Chain-of-Thought, ReAct, LangGraph
- Execution: FastAPI, Node.js, Docker
✅ Step 4: Agent Architecture
+-------------------------+
| User Goal/Input |
+------------+------------+
|
+-------v-------+
| Task Planner |
+-------+-------+
|
+-------v-------+
| Reasoner/LLM |
+-------+-------+
|
+-------v-------+
| Tool Executor |
+-------+-------+
|
+-------v-------+
| Memory Store |
+---------------+
✅ Step 5: Implement Core Functions
- Planner: Uses LLM to decompose goals
- Tool Executor: Code layer for calling APIs or RAG models
- Memory: Vector DB for past chunks and context
- Reasoner: Decides what to do next with each result
📄 Example: Document Research Agent
User Goal: “Summarize Q2 hiring policy and flag changes from Q1.”
- 🎯 Planner breaks goal into Q1/Q2 doc retrieval + comparison
- 📁 Tools: FileRetriever, Summarizer
- 🧠 Memory stores policies and summaries
- 🔁 Reasoner decides to compare results and summarize
- 📤 Output delivered with differences highlighted
🧪 Libraries & Frameworks
- LangChain: Agents, Memory, Tools, Chains
- LangGraph: LLM-driven DAG for planning
- CrewAI: Multiple collaborating agents
- OpenAgents / AutoGPT: Full autonomous agent projects
⚠️ Challenges
- 💰 LLM cost & token limits
- ⏱️ Latency in multi-step planning
- 🧢 Hallucinations if not grounded in memory
- 🔐 Need for controlled API & tool access
Want a starter repo or architecture diagram image? Contact us to start building agentic AI for your business.
