AI Agents: From Chat to Getting Work Done

ChatGPT answers when asked. An agent gets a goal and figures out how to reach it. That shift is among the most important in AI circa 2025–2026.

Chat vs. Agent

Chat:
  You → "What's the weather in Beijing today?"
  AI → "Sunny, 25°C." (from training knowledge—may be stale)

Agent:
  You → "Check Beijing weather; remind me to bring an umbrella if it rains"
  AI → call weather API → real data → decide → "Sunny, no umbrella needed"
       (or: call calendar API → create reminder)

Core difference: agents call external tools, not only internal “memory.”

The Agent Loop

Most agents follow the same pattern:

        ┌──────────┐
        │ Get goal │
        └────┬─────┘
             ↓
        ┌──────────┐
   ┌───→│  Plan    │  LLM picks next action
   │    └────┬─────┘
   │         ↓
   │    ┌──────────┐
   │    │ Use tool │  search / code / API / files
   │    └────┬─────┘
   │         ↓
   │    ┌──────────┐
   │    │ Observe  │  what did the tool return?
   │    └────┬─────┘
   │         ↓
   │    Done?
   │    no ──┘
   │    yes ↓
   └─ Final output

This is ReAct (Reasoning + Acting)—the baseline agent architecture.

What Tools Exist

Agent capability = tools you provide:

Tool type	Examples	Use
Search	Google, Bing API	Live information
Code execution	Python, shell	Compute, transform data
Files	read/write, Git	Dev workflows (Cursor’s core)
APIs	weather, email, DB	Business systems
Browser	navigate, click, fill forms	Web automation

Cursor is essentially an agent: “fix this bug” → read code, edit files, run tests.

MCP: Standardized Tool Interface

Before MCP, every product wired every tool separately. MCP (Model Context Protocol) standardizes the interface:

AI model ←→ MCP protocol ←→ tools (DB, Git, Slack, filesystem…)

Like USB for hardware—one protocol, many devices. Still early, direction is clear.

Real Limits

Do not trust demos alone:

• Runaway loops: repeats until max steps
• Bad tool calls: wrong API, bad args, too much permission
• Cost: complex tasks may loop 20×—token bill spikes
• Reliability: ~80% on simple tasks; complex workflows often below 50%

Engineering rule: start with narrow agents (e.g., read-only code review)—not “do everything” generalists on day one.

Relationship to RAG

Not either/or—often combined:

User question → agent chooses strategy
  → need docs? call RAG
  → need live data? call API
  → need math? run code
  → synthesize → answer

Summary

Agent = LLM + tools + loop. AI moves from “only speaks” to “can execute.” That loop explains Cursor, Devin, Copilot Workspace, and similar products.

Next: fine-tuning, RAG, long context—three ways to make models “know your world.” How to choose?