Building custom tools in Agentic AI - Model Pipeline Trace

Model Pipeline - Building custom tools

This pipeline shows how to build a custom AI tool that learns from data, improves over time, and makes predictions to help with specific tasks.

Data Flow - 6 Stages

1Data Collection

1000 rows x 6 columns→Gather raw data relevant to the custom tool's task→1000 rows x 6 columns

User activity logs with 6 features like time, action type, and context

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2Data Preprocessing

1000 rows x 6 columns→Clean data, handle missing values, and normalize features→1000 rows x 6 columns

Missing times filled, action types encoded as numbers, values scaled between 0 and 1

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3Feature Engineering

1000 rows x 6 columns→Create new features to better represent the data→1000 rows x 8 columns

Added features like time of day category and action frequency count

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4Model Training

800 rows x 8 columns→Train model on 80% of data to learn patterns→Trained model

Model learns to predict user intent from features

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5Model Evaluation

200 rows x 8 columns→Test model on 20% unseen data to check accuracy→Accuracy score and loss value

Model achieves 85% accuracy on test data

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6Prediction

1 row x 8 columns→Use trained model to predict output for new input→Prediction result

Model predicts user wants to schedule a meeting

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |*   
0.3 |*   
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning basic patterns
2	0.50	0.72	Loss decreases, accuracy improves
3	0.40	0.80	Model captures more complex relationships
4	0.35	0.83	Training stabilizes with good accuracy
5	0.30	0.85	Final epoch shows best performance

Prediction Trace - 4 Layers

Layer 1: Input Layer

Layer 2: Hidden Layer with ReLU

Layer 3: Output Layer with Softmax

Layer 4: Prediction

Model Quiz - 3 Questions

What happens to the data shape after feature engineering?

A It decreases from 8 to 6 columns

B It stays the same at 6 columns

C It increases from 6 to 8 columns

D It changes from rows to columns

Key Insight

Building custom tools involves preparing data carefully, training a model that improves over time, and using the model's predictions to assist with specific tasks effectively.

Practice

1. What is the main purpose of building custom tools for an AI agent?

easy

A. To change the AI's language automatically

B. To add special skills that help the AI perform specific tasks

C. To reduce the size of the AI model

D. To make the AI run faster on any computer

Solution

1. Step 1: Understand what custom tools do

   Custom tools add new abilities or skills to an AI, making it better at certain jobs.

2. Step 2: Compare options to the purpose

   Only To add special skills that help the AI perform specific tasks talks about adding special skills, which matches the purpose of custom tools.

3. Final Answer:

   To add special skills that help the AI perform specific tasks -> Option B

4. Quick Check:

   Custom tools = add special skills [OK]

Hint: Custom tools add new skills to AI for tasks [OK]

Common Mistakes:

• Thinking custom tools speed up AI generally
• Confusing tool purpose with model size
• Assuming tools change AI language automatically

2. Which of the following is the correct way to define a custom tool in Python for an AI agent?

easy

A. tool = Tool(name='search', func=search_function)

B. tool = Tool('search', func=search_function)

C. tool = Tool(description='Find info', func=search_function)

D. tool = Tool(name='search', description='Find info', func=search_function)

Solution

1. Step 1: Recall required fields for a custom tool

   A custom tool needs a name, description, and a function to work properly.

2. Step 2: Check which option includes all three

   Only tool = Tool(name='search', description='Find info', func=search_function) has name, description, and func parameters correctly set.

3. Final Answer:

   tool = Tool(name='search', description='Find info', func=search_function) -> Option D

4. Quick Check:

   Tool needs name, description, and func [OK]

Hint: Include name, description, and func when defining tools [OK]

Common Mistakes:

• Omitting description or name
• Passing parameters in wrong order
• Using wrong parameter names

3. Given this Python code for a custom tool, what will be the output when calling tool.func('hello')?

def shout(text):
    return text.upper() + '!!!'
tool = Tool(name='shout', description='Make text loud', func=shout)

medium

A. 'HELLO!!!'

B. 'hello!!!'

C. 'hello'

D. Error: func is not callable

Solution

1. Step 1: Understand the function behavior

   The function shout converts text to uppercase and adds three exclamation marks.

2. Step 2: Apply the function to 'hello'

   Calling shout('hello') returns 'HELLO!!!'. Since tool.func points to shout, tool.func('hello') does the same.

3. Final Answer:

   'HELLO!!!' -> Option A

4. Quick Check:

   shout('hello') = 'HELLO!!!' [OK]

Hint: Check function logic and apply input to predict output [OK]

Common Mistakes:

• Ignoring uppercase conversion
• Missing exclamation marks
• Assuming func is not callable

4. You wrote this custom tool but get an error when using it. What is the likely problem?

def add_numbers(a, b):
    return a + b
tool = Tool(name='adder', description='Add two numbers', func=add_numbers)
result = tool.func(5)

medium

A. Tool name must be unique

B. Function add_numbers should not return a value

C. Missing one argument when calling tool.func

D. Description is too short

Solution

1. Step 1: Check function parameters

   add_numbers requires two inputs: a and b.

2. Step 2: Check how tool.func is called

   tool.func(5) provides only one argument, causing an error for missing the second argument.

3. Final Answer:

   Missing one argument when calling tool.func -> Option C

4. Quick Check:

   Function needs 2 args, only 1 given [OK]

Hint: Match function parameters with call arguments [OK]

Common Mistakes:

• Ignoring function argument count
• Thinking description length causes error
• Assuming tool name uniqueness causes runtime error

5. You want to build a custom tool that summarizes text by returning the first 10 words. Which code correctly defines this tool's function?

hard

A. def summarize(text):
return ' '.join(text.split()[:10])

B. def summarize(text):
return text[:10]

C. def summarize(text):
return text.split()[-10:]

D. def summarize(text):
return len(text.split())

Solution

1. Step 1: Understand the goal of the function

   The function should return the first 10 words, not characters or last words.

2. Step 2: Analyze each option

   def summarize(text): return ' '.join(text.split()[:10]) splits text into words and joins the first 10 words correctly. def summarize(text): return text[:10] returns first 10 characters, not words. def summarize(text): return text.split()[-10:] returns last 10 words. def summarize(text): return len(text.split()) returns word count, not summary.

3. Final Answer:

   def summarize(text): return ' '.join(text.split()[:10]) -> Option A

4. Quick Check:

   First 10 words = def summarize(text): return ' '.join(text.split()[:10]) [OK]

Hint: Split text and join first 10 words for summary [OK]

Common Mistakes:

• Returning characters instead of words
• Taking last words instead of first
• Returning word count instead of summary