🤖 Agent কী?
Agent = LLM + Tools + Memory + Planning। Single prompt-এর বদলে, agent multi-step কাজ নিজে করতে পারে।
Chatbot:
User → LLM → Response (stateless)
Agent:
User → [Plan → Act → Observe → Repeat] → Result
↑__________ uses tools __________|
🔧 Agent Component
- LLM — brain (decision)
- Tools — hands (search, code, API)
- Memory — history, learned facts
- Planner — task decomposition
- Executor — tool orchestration
🔄 ReAct Pattern ⭐
Thought: I need latest AI news.
Action: web_search("AI news 2025")
Observation: [3 results]
Thought: First result promising, read it.
Action: read_url(results[0])
Observation: [content]
Thought: Good info, now summarize.
Final Answer: ...
🛠️ Common Tool
- Web search (Tavily, Serper, Brave)
- URL reader / scraper
- Code execution (Python sandbox)
- Database query (SQL)
- File read/write
- API call (REST/GraphQL)
- Calculator
- Calendar, email
- RAG retrieval
- Image generator
📋 Tool Definition
{
"name": "search_flights",
"description": "Search flights between two cities",
"parameters": {
"type": "object",
"properties": {
"from": { "type": "string", "description": "Origin" },
"to": { "type": "string" },
"date": { "type": "string", "format": "date" },
"passengers": { "type": "integer", "minimum": 1 }
},
"required": ["from", "to", "date"]
}
}
🧠 Planning
Complex task → subtask decomposition।
User: "Plan a 7-day trip to Japan under $3000" Agent plan: 1. Research best time to visit 2. Check flight prices (search_flight tool) 3. Find accommodation options 4. List must-see attraction 5. Budget breakdown 6. Day-by-day itinerary 7. Compile final plan Execute each step, aggregate result.
💾 Memory Type
- Short-term — conversation history
- Long-term — vector DB (facts learned)
- Episodic — past task outcome
- Semantic — general knowledge
- Procedural — how-to (skill)
🎭 Multi-Agent System ⭐
Multiple specialized agent collaborate।
Example: Content creation team - Researcher Agent → gather info - Writer Agent → draft article - Editor Agent → polish - SEO Agent → optimize - Publisher Agent → post Orchestrator coordinates all.
🏗️ Multi-Agent Framework
- AutoGen (Microsoft)
- CrewAI
- LangGraph (LangChain)
- OpenAI Swarm
- MetaGPT
- Claude Sub-agents
🌐 Agent Platform
| Platform | বৈশিষ্ট্য |
|---|---|
| OpenAI Assistants | API, hosted |
| Claude Computer Use | Browser/OS control |
| Anthropic MCP | Model Context Protocol — tool standard |
| Devin (Cognition) | Autonomous coder |
| Manus | General autonomous |
| Cursor/Windsurf Agent | Coding agent |
| n8n / Make | Workflow agent |
⚠️ Agent Challenge
- Hallucinated tool call — বানানো function
- Infinite loop — same action repeat
- Goal drift — original objective miss
- Cost explosion — too many LLM call
- Error cascade — one failure → snowball
- Security — tool misuse
- Latency — slow multi-step
🛡️ Safety Measure
- Max iteration limit (10-20)
- Timeout per step
- Tool approval gate (destructive action)
- Audit log every action
- Budget limit (token / cost)
- Sandbox code execution
- Human-in-loop checkpoint
- Output validation
💼 Agent Use Case ⭐
- Research & report
- Automated code PR
- Customer service (L2 triage)
- Data pipeline monitoring
- Invoice processing
- Travel booking
- Scheduling coordination
- QA testing
- SEO optimization
- Lead enrichment
📈 Success Pattern
- Narrow task (not "do everything")
- Clear success criteria
- Tool well-designed (good description)
- Error handling robust
- Human supervision-এ start
- Gradually autonomy বাড়ান
- Monitor + iterate
🎯 Build Your First Agent
Simple research agent (Python + LangChain):
from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI
from langchain.utilities import GoogleSearchAPIWrapper
search = GoogleSearchAPIWrapper()
tools = [
Tool(name="Search", func=search.run,
description="Search the web")
]
agent = initialize_agent(
tools, OpenAI(temperature=0),
agent="zero-shot-react-description",
verbose=True
)
agent.run("Who won 2024 Nobel Prize in Physics?")
🔮 Future
2025-2027: Agent = software's next interface। Natural language → autonomous action। Every SaaS becoming agent-enabled।