Agent Case Studies

Case 1: Code Agent

Scenario Description

Develop an Agent that can automatically write, test, and optimize code.

Agent Design

class CodeAgent:
    def __init__(self):
        self.llm = LLM()
        self.memory = Memory()
        self.tools = Tools()

        # Register tools
        self.tools.register("write_code", CodeWriter())
        self.tools.register("run_test", TestRunner())
        self.tools.register("analyze_code", CodeAnalyzer())

    def develop(self, requirement):
        # 1. Understand requirements
        understanding = self.llm.understand(requirement)
        self.memory.store("requirement", understanding)

        # 2. Write code
        code = self.tools.execute("write_code", understanding)
        self.memory.store("code", code)

        # 3. Run tests
        test_result = self.tools.execute("run_test", code)

        # 4. Analyze and optimize
        if test_result["passed"]:
            analysis = self.tools.execute("analyze_code", code)
            if analysis["needs_optimization"]:
                code = self.optimize(code, analysis)

        return code

    def optimize(self, code, analysis):
        """Optimize code"""
        optimization = self.llm.generate(
            f"Optimize the following code to address these issues: {analysis['issues']}\n{code}"
        )
        return optimization

Practical Application

Task: Develop a quicksort function

# Agent execution process
agent = CodeAgent()
requirement = """
Develop a Python quicksort function with requirements:
1. Time complexity O(n log n)
2. Space complexity O(log n)
3. Include test cases
"""

# 1. Understand requirements
understanding = agent.llm.understand(requirement)
# Output: Need to implement quicksort, focus on time and space complexity

# 2. Write code
code = agent.tools.execute("write_code", understanding)
# Output:
def quick_sort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right = [x for x in arr if x > pivot]
    return quick_sort(left) + middle + quick_sort(right)

# 3. Run tests
test_result = agent.tools.execute("run_test", code)
# Output: All tests passed

# 4. Analyze and optimize
analysis = agent.tools.execute("analyze_code", code)
# Output: Code is correct, but space complexity can be optimized

# 5. Optimize code
optimized_code = agent.optimize(code, analysis)
# Output:
def quick_sort(arr, low=0, high=None):
    if high is None:
        high = len(arr) - 1
    if low < high:
        pi = partition(arr, low, high)
        quick_sort(arr, low, pi - 1)
        quick_sort(arr, pi + 1, high)
    return arr

def partition(arr, low, high):
    pivot = arr[high]
    i = low - 1
    for j in range(low, high):
        if arr[j] <= pivot:
            i += 1
            arr[i], arr[j] = arr[j], arr[i]
    arr[i + 1], arr[high] = arr[high], arr[i + 1]
    return i + 1

Effect Evaluation

Advantages:

• ✅ Automatically writes code
• ✅ Automatically tests and verifies
• ✅ Automatically optimizes and improves
• ✅ Increases development efficiency

Limitations:

• ❌ Requires human supervision
• ❌ May generate incorrect code
• ❌ Optimization may not be perfect

Case 2: Research Agent

Scenario Description

Develop an Agent that can automatically collect, analyze, and summarize information.

Agent Design

class ResearchAgent:
    def __init__(self):
        self.llm = LLM()
        self.memory = Memory()
        self.tools = Tools()

        # Register tools
        self.tools.register("search", SearchEngine())
        self.tools.register("read", DocumentReader())
        self.tools.register("analyze", ContentAnalyzer())
        self.tools.register("summarize", Summarizer())

    def research(self, topic):
        # 1. Search relevant literature
        papers = self.tools.execute("search", topic)

        # 2. Read and analyze
        analyses = []
        for paper in papers:
            content = self.tools.execute("read", paper)
            analysis = self.tools.execute("analyze", content)
            analyses.append(analysis)

        # 3. Comprehensive summary
        summary = self.tools.execute("summarize", analyses)

        return summary

Practical Application

Task: Research the development of large language models in 2023

# Agent execution process
agent = ResearchAgent()
topic = "Development of large language models in 2023"

# 1. Search relevant literature
papers = agent.tools.execute("search", topic)
# Output: Found 50 relevant papers

# 2. Read and analyze
analyses = []
for paper in papers[:10]:  # Analyze top 10
    content = agent.tools.execute("read", paper)
    analysis = agent.tools.execute("analyze", content)
    analyses.append(analysis)

# 3. Comprehensive summary
summary = agent.tools.execute("summarize", analyses)
# Output:
"""
Major developments in large language models in 2023:

1. Continued growth in model scale
   - GPT-4 reached trillion parameters
   - Claude 2 supports 100K context
   - LLaMA 2 open-sourced 70B model

2. Significant capability improvements
   - Enhanced reasoning abilities
   - Development of multimodal capabilities
   - Improved code generation capabilities

3. Expanded application scenarios
   - Increased enterprise applications
   - Educational sector applications
   - Creative content generation

4. Emphasis on safety and ethics
   - Development of alignment techniques
   - Strengthened safety mechanisms
   - Establishment of ethical frameworks
"""

Effect Evaluation

Advantages:

• ✅ Automatically collects information
• ✅ Systematically analyzes literature
• ✅ Generates structured summaries
• ✅ Saves research time

Limitations:

• ❌ May miss important literature
• ❌ Limited analysis depth
• ❌ Requires human verification

Case 3: Creative Agent

Scenario Description

Develop an Agent that can automatically create and optimize content.

Agent Design

class CreativeAgent:
    def __init__(self):
        self.llm = LLM()
        self.memory = Memory()
        self.tools = Tools()

        # Register tools
        self.tools.register("generate", ContentGenerator())
        self.tools.register("evaluate", ContentEvaluator())
        self.tools.register("optimize", ContentOptimizer())

    def create(self, brief):
        # 1. Generate draft
        draft = self.tools.execute("generate", brief)

        # 2. Evaluate quality
        evaluation = self.tools.execute("evaluate", draft)

        # 3. Optimize and improve
        if evaluation["score"] < 0.8:
            draft = self.tools.execute("optimize", {
                "content": draft,
                "feedback": evaluation["feedback"]
            })

        return draft

Practical Application

Task: Create a product promotional copy

# Agent execution process
agent = CreativeAgent()
brief = """
Create promotional copy for a smartwatch:
- Target audience: Young professionals
- Key selling points: Health monitoring, smart reminders, stylish design
- Style: Lively, modern
"""

# 1. Generate draft
draft = agent.tools.execute("generate", brief)
# Output:
"""
This smartwatch is amazing! It can monitor your health, remind you of important things,
and has a stylish design. Young people will love it!
"""

# 2. Evaluate quality
evaluation = agent.tools.execute("evaluate", draft)
# Output:
{
    "score": 0.6,
    "feedback": [
        "Expression too simple",
        "Lacks specific scenarios",
        "Insufficient appeal"
    ]
}

# 3. Optimize and improve
optimized = agent.tools.execute("optimize", {
    "content": draft,
    "feedback": evaluation["feedback"]
})
# Output:
"""
On a busy morning, the smartwatch gently vibrates, reminding you of an upcoming important meeting.
After exercise, it precisely records your heart rate changes, helping you understand your physical condition.
On the way home from work, its stylish design makes it your trendy accessory.

This smartwatch is not just a watch, it's your intelligent life companion.
"""

Effect Evaluation

Advantages:

• ✅ Quickly generates content
• ✅ Automatically evaluates quality
• ✅ Continuously optimizes and improves
• ✅ Saves creative time

Limitations:

• ❌ Creativity may be limited
• ❌ Requires human guidance
• ❌ Insufficient personalization

Case 4: Analysis Agent

Scenario Description

Develop an Agent that can automatically analyze data and generate reports.

Agent Design

class AnalysisAgent:
    def __init__(self):
        self.llm = LLM()
        self.memory = Memory()
        self.tools = Tools()

        # Register tools
        self.tools.register("load_data", DataLoader())
        self.tools.register("analyze", DataAnalyzer())
        self.tools.register("visualize", DataVisualizer())
        self.tools.register("report", ReportGenerator())

    def analyze(self, data_source, question):
        # 1. Load data
        data = self.tools.execute("load_data", data_source)

        # 2. Analyze data
        analysis = self.tools.execute("analyze", {
            "data": data,
            "question": question
        })

        # 3. Visualize
        visualization = self.tools.execute("visualize", analysis)

        # 4. Generate report
        report = self.tools.execute("report", {
            "analysis": analysis,
            "visualization": visualization
        })

        return report

Practical Application

Task: Analyze company sales data

# Agent execution process
agent = AnalysisAgent()
data_source = "sales_data.csv"
question = "Analyze quarterly sales trends for 2023"

# 1. Load data
data = agent.tools.execute("load_data", data_source)

# 2. Analyze data
analysis = agent.tools.execute("analyze", {
    "data": data,
    "question": question
})
# Output:
{
    "trends": {
        "Q1": {"growth": 5%, "revenue": 1M},
        "Q2": {"growth": 8%, "revenue": 1.08M},
        "Q3": {"growth": 12%, "revenue": 1.21M},
        "Q4": {"growth": 15%, "revenue": 1.39M}
    },
    "insights": [
        "Sales continue to grow",
        "Q4 grew fastest",
        "Annual growth of 40%"
    ]
}

# 3. Visualize
visualization = agent.tools.execute("visualize", analysis)
# Output: Sales trend line chart

# 4. Generate report
report = agent.tools.execute("report", {
    "analysis": analysis,
    "visualization": visualization
})
# Output:
"""
2023 Sales Data Analysis Report

1. Overall Trends
   - Annual sales growth of 40%
   - Continued growth across all quarters
   - Q4 grew fastest at 15%

2. Quarterly Analysis
   - Q1: Base growth of 5%
   - Q2: Steady growth of 8%
   - Q3: Accelerated growth of 12%
   - Q4: Strong growth of 15%

3. Key Findings
   - Continued strong market demand
   - Strong product competitiveness
   - Effective marketing strategy

4. Recommendations
   - Maintain growth momentum
   - Expand market share
   - Optimize product structure
"""

Effect Evaluation

Advantages:

• ✅ Automatically analyzes data
• ✅ Generates visualizations
• ✅ Generates structured reports
• ✅ Provides insights and recommendations

Limitations:

• ❌ Limited analysis depth
• ❌ May miss details
• ❌ Requires domain knowledge

Summary

Agent practices demonstrate the powerful capabilities of AI technology:

Key Points:

• ✅ Code Agent: Automatically develops, tests, and optimizes code
• ✅ Research Agent: Automatically collects, analyzes, and summarizes information
• ✅ Creative Agent: Automatically creates, evaluates, and optimizes content
• ✅ Analysis Agent: Automatically analyzes, visualizes, and reports data

Best Practices:

1. Clearly define Agent capability boundaries
2. Design reasonable workflows
3. Establish quality evaluation mechanisms
4. Maintain human supervision
5. Continuously optimize and improve

Remember:

• Agents are tools, not replacements
• Require human guidance and verification
• Will make mistakes, need checking
• Are developing rapidly

Understanding Agent practices helps better apply AI technology and improve work efficiency.

Next Steps

• Common Agent Patterns - Learn about common Agent design patterns
• Prompt Library - Learn practical prompting techniques