AI agents can perform complex tasks, call tools, and interact with external services. But what happens when something goes wrong?
Without logging, debugging an AI agent can feel like trying to solve a puzzle with half the pieces missing.
In this lab, you'll learn how to enable logging in Strands Agents so you can observe what's happening behind the scenes, understand agent behavior, and troubleshoot issues more effectively.
🚀 What You'll Learn
By the end of this lab, you'll understand:
- How logging works in Strands Agents
- How to enable DEBUG logs
- How to inspect agent execution flow
- How to monitor tool invocations
- How logging helps troubleshoot agent issues
🤖 Why Logging Matters in Agentic AI
Traditional applications usually follow a predictable flow.
AI agents are different.
A single prompt can trigger multiple reasoning steps, tool calls, and external API requests.
User Prompt
↓
Agent Reasoning
↓
Tool Selection
↓
HTTP Request
↓
Response Processing
↓
Final Answer
If something fails during this process, logs help us understand:
- Which tool was selected
- What requests were sent
- What responses were received
- Where failures occurred
- How the agent reached its final answer
Think of logging as the flight recorder for your AI agent.
🛠️ Prerequisites
Before starting, make sure you have:
- Python 3.10+
- AWS Account
- Amazon Bedrock access
- Access to a model such as
amazon.nova-2-lite-v1 - AWS credentials configured
-
uvinstalled
📜 The Script
Let's start by looking at the complete example.
# This lab is to teach you about logging
from strands import Agent
from strands.models.bedrock import BedrockModel
from strands_tools import file_read, file_write, http_request
import logging
# Configure the root strands logger
logging.getLogger("strands").setLevel(logging.DEBUG)
# Add a handler to display logs in the terminal
logging.basicConfig(
format="%(levelname)s | %(name)s | %(message)s",
handlers=[logging.StreamHandler()]
)
# Configure the Bedrock model
bedrock_model = BedrockModel(
model_id="<YOUR_MODEL_ID>",
region_name="eu-west-2",
temperature=0.3,
)
system_prompt = """
Weather Information
- You can also make HTTP requests to the National Weather Service API.
- Process and display weather forecast data for locations in the United States.
"""
# Create the agent with tools
local_agent = Agent(
system_prompt=system_prompt,
model=bedrock_model,
tools=[http_request],
)
# Ask the agent a question
local_agent("what is the weather in new york?")
Now let's break it down.
⚙️ Step 1: Configure Logging
import logging
logging.getLogger("strands").setLevel(logging.DEBUG)
logging.basicConfig(
format="%(levelname)s | %(name)s | %(message)s",
handlers=[logging.StreamHandler()]
)
What is happening here?
First, we configure Python's built-in logging module.
logging.getLogger("strands").setLevel(logging.DEBUG)
This tells Strands to emit detailed debugging information.
The DEBUG level provides maximum visibility into agent execution.
We then configure how logs appear in the terminal:
logging.basicConfig(
format="%(levelname)s | %(name)s | %(message)s"
)
Example:
DEBUG | strands.agent | Processing user request
INFO | strands.tools | Executing HTTP request
This makes logs easy to read and troubleshoot.
⚙️ Step 2: Configure the Bedrock Model
bedrock_model = BedrockModel(
model_id="<YOUR_MODEL_ID>",
region_name="eu-west-2",
temperature=0.3,
)
Here we create the LLM that powers our agent.
Parameters
| Parameter | Description |
|---|---|
| model_id | Bedrock model identifier |
| region_name | AWS Region |
| temperature | Controls response randomness |
A lower temperature produces more consistent and predictable responses.
⚙️ Step 3: Define the Agent's Role
system_prompt = """
Weather Information
- You can also make HTTP requests to the National Weather Service API.
- Process and display weather forecast data for locations in the United States.
"""
The system prompt acts as the agent's instructions.
It tells the model:
- What task it should perform
- Which information sources are available
- How it should behave
In this case, the agent is focused on retrieving weather information.
⚙️ Step 4: Add the HTTP Tool
tools=[http_request]
The http_request tool gives the agent the ability to access external APIs.
Without tools, the agent can only rely on its training data.
With tools, it can fetch live information from the internet.
For weather data, the agent can query APIs and process real-time forecasts.
⚙️ Step 5: Create the Agent
local_agent = Agent(
system_prompt=system_prompt,
model=bedrock_model,
tools=[http_request],
)
This combines:
- The model
- The instructions
- The available tools
into a working AI agent.
▶️ Run the Agent
Invoke the agent using:
local_agent("what is the weather in new york?")
The agent will:
- Analyze the request
- Decide whether a tool is needed
- Call the HTTP tool
- Retrieve weather information
- Generate a final response
📊 Example Debug Output
When running the script, you'll see logs similar to:
DEBUG | strands.agent | Processing user request
DEBUG | strands.agent | Selecting tool
INFO | strands.tools | Executing HTTP request
INFO | strands.tools | Response received
DEBUG | strands.agent | Generating final answer
The exact output may vary depending on the model and SDK version.
🔍 Understanding the Log Levels
DEBUG
Most detailed level.
Useful during development.
DEBUG | Agent selecting tool
INFO
General execution information.
INFO | HTTP request executed
WARNING
Something unexpected happened, but execution continues.
WARNING | Missing optional parameter
ERROR
Execution failed.
ERROR | Failed to call external API
💡 Why Developers Love Logs
Imagine your agent gives an incorrect answer.
Without logs:
❌ No idea what happened.
With logs:
✅ See which tool was selected
✅ Inspect API requests
✅ Inspect responses
✅ Find failures quickly
✅ Improve prompts and tools
Logs dramatically reduce debugging time.
🎯 Key Takeaways
- Logging provides visibility into agent execution
- DEBUG logs help trace reasoning and tool usage
- Python's built-in logging module works seamlessly with Strands
- Logs make troubleshooting faster and easier
- Observability is essential for production-ready AI agents
📚 Source Code
GitHub Repository:
https://github.com/d3vjamal/strands-agents-labs
🚀 Next Lab
In the next lab, we'll explore custom tools and learn how to extend Strands Agents with capabilities tailored to your own applications.
