Test cases for tool-using agents in Agentic AI - Model Pipeline Trace

Model Pipeline - Test cases for tool-using agents

This pipeline tests how an AI agent uses external tools to complete tasks. It checks if the agent correctly selects, uses, and integrates tool outputs to improve task performance.

Data Flow - 4 Stages

1Input Task

1 task description→Receive a task that requires tool usage→1 task description

"Translate this sentence and summarize it."

↓

2Tool Selection

1 task description→Agent decides which tools to use based on task→List of selected tools

["Translation Tool", "Summarization Tool"]

↓

3Tool Execution

List of selected tools and task data→Agent sends data to tools and receives outputs→List of tool outputs

["Translated sentence", "Summary text"]

↓

4Output Integration

List of tool outputs→Agent combines tool outputs into final answer→Final response text

"Here is the translated and summarized text."

Training Trace - Epoch by Epoch

Loss
0.5 |****
0.4 |***
0.3 |**
0.2 |*
0.1 | 
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.45	0.60	Agent starts learning to select correct tools.
2	0.32	0.75	Better tool selection and output integration.
3	0.20	0.85	Agent improves in using tools effectively.
4	0.15	0.90	High accuracy in task completion with tools.
5	0.12	0.93	Training converges with stable performance.

Prediction Trace - 4 Layers

Layer 1: Receive Task

Layer 2: Tool Selection

Layer 3: Tool Execution

Layer 4: Output Integration

Model Quiz - 3 Questions

What is the agent's first action after receiving a task?

A Select which tools to use

B Execute all available tools

C Integrate outputs immediately

D Ignore the task

Key Insight

Testing tool-using agents involves checking if they can pick the right tools, use them correctly, and combine their outputs well. Training shows steady improvement in these skills, leading to better task results.