AI Agents Explained
AI agents are the next frontier—systems that don't just respond to queries but autonomously plan and execute multi-step tasks.
What Is an AI Agent?
An AI agent is:
- An LLM that can use tools
- That can plan sequences of actions
- That operates in a loop until a goal is achieved
- With varying degrees of autonomy
Chatbot: Answer questions Agent: Complete tasks
The Agent Loop
Goal: "Book a flight to Tokyo next week"
↓
[Plan]: Search flights → Compare options → Book best one
↓
[Act]: Call flight search API
↓
[Observe]: Got 5 options
↓
[Think]: Option 3 looks best - good time, reasonable price
↓
[Act]: Call booking API
↓
[Observe]: Booking confirmed
↓
[Done]: Return confirmation to user
This loop—Plan → Act → Observe → Repeat—is the core of agentic AI.
Tool Use: The Foundation
Agents need tools to interact with the world:
Common tools:
- Web search
- Code execution
- File read/write
- API calls
- Database queries
- Browser control
How it works:
- LLM decides which tool to use
- Formats the tool call correctly
- System executes the tool
- Result fed back to LLM
- LLM decides next action
Types of Agents
1. Single-Action Agents
One tool call, one response.
- "What's the weather in Tokyo?" → [Weather API] → "It's 72°F"
2. ReAct Agents
Reason and Act in alternating steps.
- Think → Act → Observe → Think → Act...
3. Plan-and-Execute Agents
Create a full plan upfront, then execute.
- Good for complex, multi-step tasks
4. Multi-Agent Systems
Multiple specialized agents collaborating.
- Researcher agent + Writer agent + Editor agent
Real-World Examples
Coding Agents
- GitHub Copilot Workspace — Plans and implements features
- Cursor — Edits code across multiple files
- Devin — Full autonomous software engineer (sort of)
Computer Use Agents
- Claude Computer Use — Controls mouse and keyboard
- OpenAI Operator — Browses web autonomously
Business Agents
- AutoGPT — General purpose task completion
- BabyAGI — Self-managing task lists
Building Agents
Frameworks
LangChain/LangGraph:
- Most flexible
- Graph-based workflows
- Lots of built-in tools
CrewAI:
- Multi-agent focus
- Role-based design
- Easy to set up
AutoGen (Microsoft):
- Multi-agent conversations
- Good for research tasks
Anthropic Claude:
- Native tool use
- Computer use capability
- MCP protocol for tools
A Simple Agent Pattern
# Pseudo-code for a basic agent
tools = [search_web, read_file, send_email]
def run_agent(goal):
messages = [{"role": "user", "content": goal}]
while True:
response = llm.generate(messages, tools=tools)
if response.has_tool_call:
result = execute_tool(response.tool_call)
messages.append({"role": "tool", "content": result})
else:
return response.content # Done!
Challenges
Reliability
Agents can:
- Get stuck in loops
- Make wrong decisions
- Misuse tools
Mitigation: Guardrails, human approval for risky actions
Cost
Agentic loops can mean many LLM calls.
10-step task × $0.05/call = $0.50 per task (adds up fast)
Safety
Autonomous systems with real-world actions need careful design:
- Sandboxed execution
- Limited permissions
- Audit trails
- Kill switches
Evaluation
How do you test something that can take variable paths?
- Define success criteria
- Track intermediate states
- Compare to human performance
The State of Agents (2024)
What works:
- Narrow, well-defined tasks
- Controlled tool sets
- Human-in-the-loop for critical decisions
Still challenging:
- Open-ended goals
- Long-running tasks
- Complex real-world interactions
The honest truth: Most production agents today have limited autonomy. Full autonomous agents are impressive demos but not yet reliable for serious work.
The Bottom Line
Agents represent AI's evolution from answering to doing:
- Start with simple tool use
- Add planning and memory
- Build toward increasing autonomy
The technology is rapidly improving. What's a demo today is production-ready tomorrow.
Next up: Multimodal AI — AI that sees, hears, and more