Agents & Tools: What They're Good For (and What to Watch For)

TL;DR

AI agents are systems that can use tools—like searching the web, calling APIs, running code, or querying databases—to complete tasks autonomously. Unlike chatbots that just respond, agents can take actions in the world. This makes them powerful for automation, but introduces new risks: mistakes, tool misuse, and unpredictable behavior.

Why it matters

Agents bridge the gap between conversation and action. They can research topics, analyze data, book appointments, or update databases without human hand-holding. But with power comes responsibility: agents need careful design, monitoring, and safety guardrails to work reliably.

What AI agents are (and aren't)

An AI agent is a system that:

• Uses an LLM to understand tasks and make decisions
• Can call tools (functions, APIs, code) to take actions
• Works autonomously, often through multiple steps
• Adapts its approach based on results

Agent vs. chatbot

• Chatbot: You ask, it answers. Pure conversation.
• Agent: You ask, it does things—searches the web, runs code, queries databases, calls APIs—then reports back.

Example:

• Chatbot: "What's the weather in Seattle?"
  • Response: "I don't have real-time data, but Seattle is usually rainy."
• Agent: "What's the weather in Seattle?"
  • Calls a weather API → Gets current data → Responds: "It's 52°F and raining in Seattle right now."

Agents aren't just smarter chatbots—they're systems that take action.

How agents use tools

Tools extend what an LLM can do. The LLM decides when and how to use each tool.

Common tool types

• Web search: Look up current info (news, prices, facts)
• APIs: Check weather, send emails, query databases, book flights
• Code execution: Run Python, SQL, or shell commands
• File operations: Read, write, or search documents
• Calculators: Solve math problems accurately
• Databases: Query or update records

How it works (function calling)

1. User asks: "What's 15% of $4,238?"
2. LLM thinks: "I need a calculator."
3. Agent calls tool: calculate(0.15 * 4238)
4. Tool returns: 635.7
5. LLM responds: "15% of $4,238 is $635.70."

Most LLM APIs support function calling (also called "tool use")—a way for the model to request structured tool invocations.

Jargon: "Function calling"
A feature where an LLM can output structured requests to call specific tools, rather than just generating text. The agent code interprets these requests and executes the tools.

Common agent patterns

1. ReAct (Reason + Act)

The agent alternates between reasoning and acting:

1. Think: What do I need to do?
2. Act: Use a tool
3. Observe: What happened?
4. Think: What's next?
5. Repeat until done

Example:

• Task: "Find the CEO of Anthropic and tell me their background."
• Think: "I need to search for Anthropic's CEO."
• Act: Web search → "Who is the CEO of Anthropic?"
• Observe: "Dario Amodei"
• Think: "Now I need his background."
• Act: Web search → "Dario Amodei background"
• Observe: "Former VP of Research at OpenAI, PhD in computational neuroscience..."
• Think: "I have enough info."
• Respond: "Dario Amodei is the CEO of Anthropic. He has a PhD in computational neuroscience and was previously VP of Research at OpenAI."

ReAct is the most popular agent pattern. It's simple, interpretable, and works well for multi-step tasks.

2. Planning and execution

The agent creates a plan upfront, then executes each step:

1. Break the task into steps
2. Execute step 1 → Execute step 2 → Execute step 3
3. Return results

Example:

• Task: "Summarize last week's sales data."
• Plan:
  1. Query the sales database for the past 7 days
  2. Calculate total revenue
  3. Identify top-selling products
  4. Write a summary
• Execute each step
• Return the summary

This works well for structured, predictable workflows.

3. Autonomous loops

The agent runs continuously, deciding when to stop:

• Keep taking actions until the task is complete
• No pre-set number of steps

Use with caution: Loops can run forever or do unexpected things.

Real-world use cases

1. Research assistants

"Find me 5 recent papers on protein folding and summarize their key findings."

• Searches academic databases
• Retrieves papers
• Summarizes each
• Compiles a report

2. Data analysis

"Analyze our Q4 sales data and tell me which regions underperformed."

• Queries the database
• Runs statistical analysis
• Generates charts (via code execution)
• Reports insights

3. Customer support

"I need to update my shipping address."

• Authenticates the user
• Queries the order database
• Updates the address via API
• Confirms the change

4. Workflow automation

"Every Monday, pull our support tickets and email a summary to the team."

• Scheduled trigger
• Queries ticket system API
• Summarizes with LLM
• Sends email via SMTP

5. Code assistants

"Fix the bug in auth.py where users can't reset passwords."

• Reads the file
• Analyzes the code
• Identifies the issue
• Suggests or writes a fix
• Optionally runs tests

When agents work well

Agents excel when tasks are:

• Multi-step (research, analysis, workflows)
• Tool-dependent (need APIs, databases, code)
• Repetitive (same logic, different inputs)
• Well-scoped (clear success criteria)

Examples:

• "Summarize today's top 10 Hacker News posts."
• "Check our server status and restart any failing services."
• "Generate a monthly expense report from our accounting system."

When agents struggle

Agents fail when tasks are:

• Ambiguous (vague goals, unclear success)
• Highly creative (subjective, nuanced)
• Safety-critical (medical, legal, financial decisions without oversight)
• Too open-ended (no clear stopping point)

Examples:

• "Make our website better." (Too vague)
• "Write a novel." (Too creative, too long)
• "Diagnose my symptoms and prescribe treatment." (Safety-critical, requires expertise)

Risks and challenges

1. Tool misuse

Agents can call the wrong tool or use tools incorrectly:

• Deleting files instead of reading them
• Querying production databases instead of staging
• Sending emails to the wrong recipients

Mitigation: Sandboxing, read-only modes, explicit permissions.

2. Runaway behavior

Autonomous loops can spiral:

• Endless tool calls
• Retrying failed actions indefinitely
• Wasting API credits

Mitigation: Step limits, timeouts, cost caps.

3. Security vulnerabilities

Agents can be exploited:

• Prompt injection: Malicious input tricks the agent into unintended actions
• Tool abuse: Agent uses tools to access unauthorized data

Mitigation: Input validation, least-privilege tool access, auditing.

4. Hallucinations and errors

LLMs can hallucinate facts or misunderstand context, leading to:

• Wrong API calls
• Bad calculations
• Incorrect conclusions

Mitigation: Verification steps, human review, structured outputs.

5. Unpredictable decision-making

Agents don't always do what you expect:

• LLMs are probabilistic, not deterministic
• Edge cases can lead to strange behavior

Mitigation: Testing, logging, monitoring, clear instructions.

Safety techniques

1. Sandboxing

Run agents in isolated environments:

• Can't access production systems
• Limited file system access
• Restricted network access

Think of it like a playground—agents can experiment without breaking things.

2. Guardrails

Set boundaries on what agents can do:

• Tool whitelists: Only allow specific tools
• Rate limits: Cap the number of API calls
• Input validation: Block malicious or nonsensical inputs
• Output filtering: Check responses before showing them

3. Human-in-the-loop

Require human approval for high-risk actions:

• "I'm about to delete 500 records. Confirm?"
• "Send this email to 10,000 customers? (Y/N)"

Keeps humans in control for critical decisions.

4. Monitoring and logging

Track what agents do:

• Log every tool call
• Monitor for anomalies (sudden cost spikes, failed actions)
• Set up alerts for risky behavior

You can't fix what you can't see.

5. Graceful degradation

Design agents to fail safely:

• If a tool fails, try an alternative or ask for help
• Don't crash—return a useful error message
• Avoid cascading failures

Building a simple agent

Here's how to build a basic agent with LangChain (Python):

from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI
from langchain.tools import DuckDuckGoSearchRun

# Define tools
search = DuckDuckGoSearchRun()
tools = [
    Tool(
        name="Web Search",
        func=search.run,
        description="Search the web for current information"
    )
]

# Initialize LLM
llm = OpenAI(temperature=0)

# Create agent
agent = initialize_agent(tools, llm, agent="zero-shot-react-description")

# Run agent
response = agent.run("What's the current price of Bitcoin?")
print(response)

What happens:

1. You ask: "What's the current price of Bitcoin?"
2. The agent thinks: "I need current data—use web search."
3. Calls the search tool
4. Gets results
5. Responds: "Bitcoin is currently trading at $43,250."

Debugging agents

When things go wrong:

1. Check the logs: What tools were called? What were the inputs/outputs?
2. Inspect the prompt: Is the task clear? Are tool descriptions accurate?
3. Test tools separately: Do they work in isolation?
4. Add verbosity: Many frameworks have debug modes that show reasoning steps
5. Simplify: Break complex tasks into smaller ones

Key terms (quick reference)

• Agent: An AI system that uses tools to complete tasks autonomously
• Tool: A function, API, or capability the agent can invoke
• Function calling: LLM feature for requesting structured tool invocations
• ReAct: Reason-Act-Observe pattern for agent decision-making
• Sandboxing: Running agents in isolated environments for safety
• Human-in-the-loop: Requiring human approval for high-risk actions
• Prompt injection: Malicious input that tricks an agent into unintended behavior

Use responsibly

• Start with read-only tools (search, databases) before allowing writes or deletes
• Test extensively in safe environments before deploying
• Monitor costs (agents can rack up API bills quickly)
• Don't use agents for life-or-death decisions without expert oversight
• Be transparent (tell users when they're interacting with an agent)
• Audit regularly (review logs, check for misuse or errors)

What's next?

• Guardrails & Policy: How to set boundaries for AI systems
• Evaluating AI Answers: Check agent outputs for accuracy
• Orchestration Options: Frameworks and tools for building agents
• Embeddings & RAG: Give agents access to your documents and knowledge bases