AI Agents & Tool Use

From Chatbots to Agents

Traditional chatbots are reactive—they respond to prompts but don't take actions. AI agents go further: they can reason, plan, use tools, and act in the world to accomplish goals.

What Makes an Agent?

An agent has:

1. Goal: What it's trying to achieve
2. Perception: How it understands its environment
3. Reasoning: How it decides what to do
4. Action: How it affects the world
5. Memory: What it remembers from past interactions

Put together, agents can work autonomously on complex tasks.

The Agent Loop

Most agents follow a core loop:

Observe → Think → Act → Observe → Think → Act → ...

1. Observe: Gather information (user input, tool outputs, environment state)
2. Think: Reason about what to do next
3. Act: Execute an action (call a tool, respond, make changes)
4. Repeat until the goal is achieved

Tool Use

Tools extend what agents can do:

• Web search: Find current information
• Code execution: Run Python, JavaScript, etc.
• APIs: Access external services
• File operations: Read, write, modify files
• Browser control: Navigate websites, fill forms
• Databases: Query and update data

The LLM decides when and how to use tools based on the task.

How Tools Work

A typical tool call looks like:

1. Agent receives task: "What's the weather in Tokyo?"
2. Agent decides: "I need the weather tool"
3. Agent calls: weather(city="Tokyo")
4. Tool returns: {"temp": 22, "condition": "sunny"}
5. Agent responds: "It's 22°C and sunny in Tokyo!"

The agent learns to use tools through training or few-shot examples.

Planning and Decomposition

Complex tasks require planning:

Task: "Write a research report on AI in healthcare"

Plan:

1. Search for recent AI healthcare papers
2. Identify key themes and applications
3. Find statistics on adoption
4. Outline report structure
5. Write each section
6. Review and refine

Good agents break big tasks into manageable steps.

Memory Systems

Agents need different types of memory:

Short-term (working memory): Current conversation, recent actions

Long-term: Past interactions, learned facts, user preferences

Episodic: Specific past experiences for reference

Memory enables continuity across sessions and learning from experience.

ReAct: Reasoning + Acting

The ReAct pattern interleaves reasoning and acting:

Thought: I need to find the population of France
Action: search("France population 2024")
Observation: France population is 68 million
Thought: Now I have the information
Action: respond("France has a population of 68 million")

Explicit reasoning traces help with debugging and transparency.

Multi-Agent Systems

Complex tasks can use multiple specialized agents:

• Researcher: Finds information
• Coder: Writes and tests code
• Critic: Reviews and critiques
• Orchestrator: Coordinates the others

Agents can debate, collaborate, and divide labor.

Reflection and Self-Improvement

Advanced agents can evaluate their own work:

1. Generate an answer
2. Critique it: "Is this correct? Complete? Well-structured?"
3. Identify weaknesses
4. Improve and iterate

This self-reflection loop improves output quality.

Challenges

Reliability: Agents can hallucinate, use wrong tools, or get stuck in loops

Safety: Autonomous actions can cause harm

Cost: Many API calls add up quickly

Evaluation: Hard to measure agent performance objectively

Sandboxing: Preventing agents from causing damage

Current Agent Frameworks

• LangChain / LangGraph: Popular toolkit for building LLM agents with graph workflows
• AutoGPT / GPT-Engineer: Autonomous agents that set and pursue goals
• CrewAI: Multi-agent collaboration framework
• OpenAI Assistants API: Built-in tool use, code interpreter, retrieval
• Claude Tool Use: Native function calling with parallel tool execution
• AutoGen (Microsoft): Multi-agent conversation framework
• Anthropic Computer Use: Agents that control desktop applications

The Future of Agents

We're moving toward agents that can:

• Work for hours or days autonomously
• Collaborate with humans and other agents
• Learn from experience
• Handle real-world uncertainty
• Know when to ask for help