Chatbots answer questions. Agents complete tasks.

That distinction matters. An AI agent doesn’t just generate text—it reasons about problems, uses tools, takes actions, and works toward goals autonomously. This changes what’s possible to automate.

What Makes an Agent Different

A standard LLM interaction:

  • You send a prompt
  • Model returns a response
  • Done

An agent interaction:

  • You define a goal
  • Agent plans an approach
  • Agent uses tools (search, code execution, APIs)
  • Agent evaluates results
  • Agent iterates until goal is achieved
  • Returns final output

The agent loop continues until the task is complete or it determines it can’t proceed.

Core Components of AI Agents

1. The Reasoning Engine

The LLM (Claude, GPT-4, etc.) serves as the brain. It:

  • Understands the goal
  • Decides which tools to use
  • Interprets results
  • Plans next steps
  • Knows when to stop

Better models = better agents. Reasoning capability matters more than raw speed.

2. Tools

Tools extend what an agent can do beyond text generation:

Information tools:

  • Web search
  • Database queries
  • File reading
  • API calls

Action tools:

  • Code execution
  • File writing
  • Email sending
  • System commands

Integration tools:

  • CRM updates
  • Calendar management
  • Spreadsheet editing
  • Deployment triggers

The agent decides when to use which tool based on the task.

3. Memory

Agents need context that persists:

Short-term memory - The current conversation and task state

Long-term memory - Information that persists across sessions (user preferences, past interactions, learned patterns)

External memory - Vector databases and document stores for retrieving relevant information

4. Planning

Complex tasks require breaking down into steps:

Goal: "Analyze our competitor's pricing and create a comparison report"

Agent's plan:
1. Search for competitor's pricing page
2. Extract pricing tiers and features
3. Retrieve our current pricing from database
4. Compare features at each price point
5. Generate comparison table
6. Write analysis summary
7. Format as PDF report

The agent executes each step, adjusting the plan based on what it learns.

Practical Agent Architectures

Single Agent

One agent handles everything. Works for focused tasks:

  • Research assistant
  • Code reviewer
  • Data analyst
  • Customer support

Multi-Agent Systems

Multiple specialized agents collaborate:

  • Researcher - Gathers information
  • Analyst - Processes data
  • Writer - Creates content
  • Reviewer - Checks quality

Agents pass work between each other, each contributing their specialty.

Human-in-the-Loop

Agent does work, human approves critical actions:

Agent: "I've drafted the customer email and found their order history.
Ready to send. Approve? [Yes/No/Edit]"

Essential for high-stakes actions until you trust the agent fully.

Building Your First Agent

Using Claude with Tool Use

import anthropic

client = anthropic.Anthropic()

tools = [
    {
        "name": "search_database",
        "description": "Search the customer database",
        "input_schema": {
            "type": "object",
            "properties": {
                "query": {"type": "string"}
            },
            "required": ["query"]
        }
    },
    {
        "name": "send_email",
        "description": "Send an email to a customer",
        "input_schema": {
            "type": "object",
            "properties": {
                "to": {"type": "string"},
                "subject": {"type": "string"},
                "body": {"type": "string"}
            },
            "required": ["to", "subject", "body"]
        }
    }
]

# Agent loop
messages = [{"role": "user", "content": "Find customers who haven't ordered in 90 days and send them a win-back email"}]

while True:
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=4096,
        tools=tools,
        messages=messages
    )

    if response.stop_reason == "end_turn":
        break  # Agent finished

    if response.stop_reason == "tool_use":
        # Execute tool and continue
        tool_results = execute_tools(response.content)
        messages.append({"role": "assistant", "content": response.content})
        messages.append({"role": "user", "content": tool_results})

Using Existing Frameworks

LangChain - Popular framework with pre-built components CrewAI - Multi-agent orchestration AutoGen - Microsoft’s multi-agent framework Claude Code - Anthropic’s coding agent

These handle the loop, memory, and tool orchestration so you focus on the task logic.

Real Business Applications

Customer Support Agent

  • Reads incoming ticket
  • Searches knowledge base
  • Checks customer history
  • Drafts response
  • Escalates complex issues to humans

Research Agent

  • Takes a research question
  • Searches multiple sources
  • Cross-references information
  • Synthesizes findings
  • Cites sources properly

Data Processing Agent

  • Monitors incoming data
  • Cleans and validates
  • Transforms to required format
  • Loads into destination system
  • Reports anomalies

Content Agent

  • Receives topic brief
  • Researches subject
  • Creates outline
  • Writes draft
  • Checks facts
  • Formats for platform

Code Agent

  • Receives feature description
  • Reads existing codebase
  • Plans implementation
  • Writes code
  • Runs tests
  • Iterates on failures

Common Pitfalls

Unbounded Loops

Agents can get stuck. Always implement:

  • Maximum iteration limits
  • Timeout handling
  • Cost caps (API calls add up)

Tool Overload

Too many tools confuse the agent. Start minimal:

  • 3-5 tools maximum initially
  • Add more only when needed
  • Clear, specific tool descriptions

Vague Goals

“Make the report better” fails. “Add a summary section with three key takeaways” succeeds. Specific goals produce specific results.

Insufficient Error Handling

Tools fail. APIs timeout. Data is malformed. Your agent needs graceful handling:

If search fails: Try alternative query
If API times out: Retry with backoff
If data is missing: Report gap, continue with available info

No Human Oversight

For critical actions, require approval:

  • Financial transactions
  • Customer communications
  • Data deletions
  • External API calls with side effects

Build trust gradually, then expand autonomy.

Measuring Agent Performance

Track these metrics:

  • Task completion rate - Did it finish successfully?
  • Steps to completion - Efficiency of approach
  • Tool usage accuracy - Right tools for the job?
  • Error recovery - Handled failures gracefully?
  • Cost per task - API spend + compute
  • Time to completion - Speed matters

Getting Started

  1. Pick one repetitive task you do weekly
  2. Break it into steps you could explain to an assistant
  3. Identify the tools needed (search, APIs, file operations)
  4. Build a minimal agent with those tools
  5. Run it supervised until you trust the output
  6. Gradually increase autonomy

Start small. A working agent that saves 2 hours weekly beats an ambitious one that never ships.

The Future

Agents are early. Current limitations:

  • Reasoning errors on complex tasks
  • High latency for multi-step work
  • Cost adds up at scale
  • Reliability varies

These improve rapidly. What’s experimental today becomes standard practice tomorrow.

The builders who learn agent patterns now will have significant advantages as the technology matures.

Don’t wait for perfect. Start building.