๐๐ ๐๐ด๐ฒ๐ป๐๐: ๐ง๐ต๐ฒ ๐๐๐๐๐ฟ๐ฒ ๐ผ๐ณ ๐๐๐๐ผ๐ป๐ผ๐บ๐ผ๐๐ ๐๐ป๐๐ฒ๐น๐น๐ถ๐ด๐ฒ๐ป๐ฐ๐ฒ
AI agents see their environment. They make decisions. They take action to reach a goal. You do not need to help them.
Chatbots answer questions. Agents do more.
- They search the web.
- They run code.
- They read files.
- They call APIs.
- They plan tasks.
The secret is autonomy. Agents use a loop called ReAct.
- Observe.
- Think.
- Act.
- Repeat.
This loop runs hundreds of times. It works across tools without you in the middle.
You have three main options:
- Single agent: Fast to build. Limited scope.
- Multi-agent: Specialized roles. High capability.
- Agentic workflows: Fixed steps. High reliability.
Common uses include:
- Code: GitHub Copilot and Cursor.
- Support: Bots solving issues.
- Research: Summarizing reports.
- DevOps: Fixing pipelines.
- Content: Publishing posts.
Frameworks to use:
- LangChain.
- LlamaIndex.
- AutoGen.
- CrewAI.
- Claude with MCP.
MCP is a standard way to connect AI to tools. It lets agents plug into data without hard coding.
Agents have flaws:
- Hallucinations: Wrong output.
- Infinite loops: Spinning forever.
- Cost: High token use.
- Security: Large attack surface.
Design for failure. Focus on these three things:
- Long term planning.
- Persistent memory.
- Self-correction.
Software is changing. You no longer operate tools. Systems operate for you.
Source: https://dev.to/kielltampubolon/ai-agents-the-future-of-autonomous-intelligence-onp