AI Agents & Tool Use: From Chatbots to Autonomous Task Completion

ChatGPT answers questions. AI Agents complete tasks. The shift from passive Q&A to autonomous execution is the biggest transformation in AI since the transformer. And it's happening right now.

The ReAct Framework

Modern AI agents use ReAct (Reasoning + Acting), interleaving thought and action:

User: "What's the population of France times the GDP per capita of Germany?"

Agent Thought: I need two pieces of information. Let me search for each.

Agent Action: search("population of France 2024")
Observation: France population is approximately 68 million.

Agent Thought: Good. Now I need Germany's GDP per capita.

Agent Action: search("GDP per capita Germany 2024")  
Observation: Germany's GDP per capita is approximately $51,000.

Agent Thought: Now I can calculate: 68,000,000 × 51,000

Agent Action: calculator("68000000 * 51000")
Observation: 3,468,000,000,000

Agent Response: The population of France (68 million) multiplied by 
Germany's GDP per capita ($51,000) equals $3.468 trillion.

🔧 Function Calling: The Technical Foundation

OpenAI, Anthropic, and Google all support function/tool calling:

tools = [
    {
        "type": "function",
        "function": {
            "name": "search_database",
            "description": "Search company database for records",
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {"type": "string"},
                    "table": {"type": "string", "enum": ["customers", "orders", "products"]},
                    "limit": {"type": "integer", "default": 10}
                },
                "required": ["query", "table"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="gpt-4-turbo",
    messages=[{"role": "user", "content": "Find our top 5 customers by revenue"}],
    tools=tools,
    tool_choice="auto"
)

The model outputs structured JSON that your code executes:

{
    "tool_calls": [{
        "function": {
            "name": "search_database",
            "arguments": "{\"query\": \"ORDER BY revenue DESC\", \"table\": \"customers\", \"limit\": 5}"
        }
    }]
}

🏗️ Agent Architectures

1. Single-Agent Loop

One LLM handles planning, tool selection, and synthesis:

while not task_complete:
    thought = llm.think(observation)
    action = llm.select_action(thought, tools)
    observation = execute(action)
    if llm.is_complete(observation):
        return llm.synthesize(history)

2. Multi-Agent Systems

Specialized agents collaborate:

• Planner Agent: Decomposes task into subtasks
• Researcher Agent: Gathers information
• Executor Agent: Takes actions
• Critic Agent: Validates outputs

3. Hierarchical Agents

Manager agents delegate to worker agents:

ManagerAgent
├── ResearchTeam
│   ├── WebSearcher
│   └── DocumentAnalyzer
├── ExecutionTeam
│   ├── CodeWriter
│   └── Deployer
└── QATeam
    ├── Tester
    └── Reviewer

⚠️ Challenges & Risks

Hallucinated Actions

Agents can confidently take wrong actions. A coding agent might "fix" a bug by deleting critical code.

Runaway Execution

Without proper safeguards, agents can:

• Send thousands of emails
• Make expensive API calls
• Modify production databases
• Create infinite loops

Security Vulnerabilities

Agents with tool access are prime targets for prompt injection (see our security post).

Building Production Agents

Frameworks for building agents:

• LangChain: Most popular, extensive tool ecosystem
• AutoGPT: Fully autonomous agents
• CrewAI: Multi-agent orchestration
• Microsoft AutoGen: Conversational agents

📚 Further Reading

• Yao et al. (2023). "ReAct: Synergizing Reasoning and Acting in Language Models"
• Significant Gravitas. "AutoGPT: An Autonomous GPT-4 Experiment"
• OpenAI. "Function Calling and Other API Updates"
• Anthropic. "Tool Use with Claude"