/aws-observability

Enable AWS Application Signals for Python on EC2

Your task is to modify Infrastructure as Code (IaC) files to enable AWS Application Signals for a Python application running on EC2 instances. You will update IAM permissions, install monitoring agents, and configure OpenTelemetry instrumentation through UserData scripts.

What You Will Accomplish

After completing this task:

• The EC2 instance will have permissions to send telemetry data to CloudWatch
• The CloudWatch Agent will be installed and configured for Application Signals
• The Python application will be automatically instrumented with AWS Distro for OpenTelemetry (ADOT)
• Traces, metrics, and performance data will appear in the CloudWatch Application Signals console
• The user will be able to see service maps, SLOs, and application performance metrics without manual code instrumentation

Critical Requirements

Error Handling:

• If you cannot determine required values from the IaC, STOP and ask the user
• For multiple EC2 instances, ask which one(s) to modify
• Preserve all existing UserData commands; add new ones in sequence

Do NOT:

• Run deployment commands automatically (cdk deploy, terraform apply, etc.)
• Remove existing application startup logic
• Skip the user approval step before deployment

IaC Tool Support

Code examples use CDK TypeScript syntax. If you are working with Terraform or CloudFormation, translate the CDK syntax to the appropriate format while keeping all bash commands identical. The UserData bash commands (CloudWatch Agent installation, ADOT installation, environment variables) are universal across all IaC tools - only the wrapper syntax differs.

Before You Start: Gather Required Information

Execute these steps to collect the information needed for configuration:

Step 1: Determine Deployment Type

Read the UserData script and look for the application startup command. This is typically one of the last commands in UserData.

If you see:

• docker run or docker start → Docker deployment
• python, gunicorn, uvicorn, flask run, or similar → Non-Docker deployment

If unclear:

• Ask the user: "Is your Python application running in a Docker container or directly on the EC2 instance?" DO NOT GUESS

Critical distinction: Where does the Python process run?

• Docker: Python runs inside a container → Modify Dockerfile
• Non-Docker: Python runs directly on EC2 → Modify UserData

Step 2: Extract Placeholder Values

Analyze the existing IaC to determine these values for Application Signals enablement:

• {{SERVICE_NAME}}:
  • Why It Matters: Sets the service name displayed in Application Signals console via OTEL_RESOURCE_ATTRIBUTES=service.name={{SERVICE_NAME}}
  • How to Find It: Use the application name, stack name, or construct ID. Look for service/app names in the IaC.
  • Example Value: my-python-app
  • Required For: Both Docker and non-Docker
• {{ENTRY_POINT}}
  • Why It Matters: Used to wrap the application startup with OpenTelemetry instrumentation: opentelemetry-instrument python {{ENTRY_POINT}}
  • How to Find It: Find the Python file that starts the application (look for python commands in UserData)
  • Example Value: app.py or main.py
  • Required For: non-Docker
• {{APP_DIR}}
  • Why It Matters: Python needs to run from the correct directory to find application files and dependencies
  • How to Find It: Find where the application code is deployed (look for cd, git clone, or file copy commands in UserData)
  • Example Value: /opt/myapp
  • Required For: non-Docker

For Docker-based deployments you will also need to find these additional values:

• {{PORT}}
  • Why It Matters: Docker port mapping that ensures the container is accessible on the correct port
  • How to Find It: Find port mappings in docker run -p commands or security group ingress rules
  • Example Value: 5000
  • Required For: Docker
• {{APP_NAME}}
  • Why It Matters: Used to reference the container for operations like docker logs {{APP_NAME}}, docker exec, health checks, etc.
  • How to Find It: Find container name in docker run --name or use {{SERVICE_NAME}}-container
  • Example Value: python-flask-app
  • Required For: Docker
• {{IMAGE_URI}}
  • Why It Matters: This is the identifier for the application that Docker will run
  • How to Find It: Find the Docker image in docker run or docker pull commands
  • Example Value: 123456789012.dkr.ecr.us-east-1.amazonaws.com/my-app:latest
  • Required For: Docker

If you cannot determine a value: Ask the user for clarification before proceeding. Do not guess or make up values.

Step 3: Identify Python Framework

Search the IaC UserData and application files for framework indicators:

• Django: django, manage.py, DJANGO_SETTINGS_MODULE, settings.py
• Flask: flask, Flask(, @app.route
• FastAPI: fastapi, FastAPI(, uvicorn
• WSGI Server: gunicorn, uwsgi in startup commands or requirements.txt
• Other: Generic Python application

If you cannot determine a value: Ask the user for clarification before proceeding. Do not guess or make up values.

Step 4: Framework-Specific Requirements

Only complete the relevant subsections based on what you identified in Step 3.

4a. Django Applications

If you identified Django in Step 3, extract the Django settings module path:

• {{DJANGO_SETTINGS_MODULE}}: The Python module path to settings.py
  • How to Find: Look for existing DJANGO_SETTINGS_MODULE in UserData/Dockerfile, or search for settings.py location
  • Common Patterns: myproject.settings (if settings.py at myproject/settings.py)
  • If not found: Ask the user for the Django settings module path

4b. WSGI Server Applications (Gunicorn/uWSGI)

If you identified a WSGI server in Step 3, note that additional worker instrumentation is required:

• Gunicorn requires a post_fork hook in gunicorn.conf.py
• uWSGI requires import directive in uwsgi.ini
• Both require OTEL_AWS_PYTHON_DEFER_TO_WORKERS_ENABLED=true environment variable
• Implementation details are covered in the Docker/non-Docker configuration sections below

Step 5: Identify Instance OS

Determine the operating system to use the correct package manager and installation commands.

Amazon Linux:

• Amazon Linux 2: Use yum package manager
• Amazon Linux 2023: Use dnf package manager
• How to detect: Look for existing package install commands in UserData (check for yum or dnf), or look for AMI references containing al2 or al2023

Other Linux distributions:

• Ubuntu/Debian: Use apt package manager
• Fedora/RHEL/CentOS: Use dnf or yum package manager

If unclear: Look for AMI name/ID in the IaC or ask the user which OS the EC2 instance is running. Do not guess or make up values.

Instructions

Follow these steps in sequence:

Step 1: Locate the IaC Files

Search for EC2 instance definitions using these patterns:

CDK:

new ec2.Instance(
ec2.Instance(
CfnInstance(

Terraform:

resource "aws_instance"

CloudFormation:

AWS::EC2::Instance

Read the file(s) containing the EC2 instance definition. You need to identify:

1. The instance resource/construct
2. The IAM role attached to the instance
3. The UserData script or property

Step 2: Locate the IAM Role

Find the IAM role attached to the EC2 instance.

CDK:

role: someRole
new iam.Role(this, 'RoleName'

Step 3: Update the IAM Role

What this policy is for, and its scope. CloudWatchAgentServerPolicy goes on the instance role for the CloudWatch agent, which receives telemetry locally and forwards it to CloudWatch and X-Ray — so these permissions are what let the agent reach those destinations, not something the instrumentation itself needs. On EC2, ECS, and EKS, an ADOT-SDK-only setup adds no IAM to the workload at all (see the setting-up-cloudwatch-observability skill's references/cloudwatch-omni/instrumentation/instrumentation.md, which forbids attaching this policy on that path). Lambda is the exception — that path does grant its execution role X-Ray write permissions.

The policy is broader than this configuration needs: it grants 14 actions, all on Resource: "*", including ec2:DescribeVolumes and logs:PutRetentionPolicy, which an Application-Signals-only agent config does not use. If the customer wants to trim it, resource scoping is the more valuable axis than pruning actions — dropping actions still leaves logs:PutLogEvents and logs:CreateLogGroup on every log group in the account and cloudwatch:PutMetricData on every namespace, so the instance role can still write over unrelated services' logs. Scope the resources and add an aws:ResourceAccount condition, the way CloudWatchLambdaApplicationSignalsExecutionRolePolicy does for the Lambda path.

Read the live document before changing anything — it takes two calls, since the version id is required and is not knowable up front: aws iam get-policy --policy-arn arn:aws:iam::aws:policy/CloudWatchAgentServerPolicy --query Policy.DefaultVersionId then aws iam get-policy-version --policy-arn <same> --version-id <that>. Do not hand-roll an action list from this page: the agent still creates the Application Signals log group (/aws/application-signals/data), so logs:CreateLogGroup and logs:CreateLogStream must survive any trim. A denial does not surface in the console — the agent records AccessDenied in its own log (/opt/aws/amazon-cloudwatch-agent/logs/amazon-cloudwatch-agent.log on Linux), and the symptom is that telemetry never starts arriving. Check that file first.

Add the CloudWatch Agent Server Policy to the IAM role's managed policies.

CDK:

const role = new iam.Role(this, 'AppRole', {
  assumedBy: new iam.ServicePrincipal('ec2.amazonaws.com'),
  managedPolicies: [
    iam.ManagedPolicy.fromAwsManagedPolicyName('CloudWatchAgentServerPolicy'),
    // ... keep existing policies
  ],
});

Step 4: Modify UserData - Add Prerequisites

Add a CloudWatch Agent installation command to the UserData script.

CRITICAL for Terraform Users: When modifying Terraform user_data heredocs, you MUST preserve the EXACT indentation of existing lines. Terraform's <<-EOF syntax strips leading whitespace, but only if indentation is consistent. When adding new bash commands:

• Count the leading spaces/tabs on existing lines in the heredoc
• Apply the SAME amount of leading whitespace to all new lines you add
• Do NOT modify the indentation of any existing lines

If indentation is inconsistent, Terraform will NOT strip the whitespace, causing the deployed script to have leading spaces before #!/bin/bash, which will cause cloud-init to fail.

CDK TypeScript example:

instance.userData.addCommands(
  'dnf install -y amazon-cloudwatch-agent',  // Use dnf for AL2023, yum for AL2
  // ... rest of UserData follows
);

Placement: Add this command early in the UserData script:

• If system update commands exist (like dnf update -y, apt-get update), add it immediately after those
• If no system update commands exist, add it at the very beginning of UserData
• This should come before any application dependency installations or application setup commands

For other Linux distributions: CloudWatch Agent may not be available via the OS package manager. Refer to AWS CloudWatch Agent installation docs for distribution-specific instructions.

Step 5: Modify UserData - Configure CloudWatch Agent

The CloudWatch Agent was installed in Step 4. Now configure it for Application Signals:

CDK TypeScript example:

instance.userData.addCommands(
  '# Create CloudWatch Agent configuration for Application Signals',
  "cat > /opt/aws/amazon-cloudwatch-agent/etc/amazon-cloudwatch-agent.json << 'EOF'",
  '{',
  '  "traces": {',
  '    "traces_collected": {',
  '      "application_signals": {}',
  '    }',
  '  },',
  '  "logs": {',
  '    "metrics_collected": {',
  '      "application_signals": {}',
  '    }',
  '  }',
  '}',
  'EOF',
  '',
  '# Start CloudWatch Agent with Application Signals configuration',
  '/opt/aws/amazon-cloudwatch-agent/bin/amazon-cloudwatch-agent-ctl \\',
  '  -a fetch-config \\',
  '  -m ec2 \\',
  '  -s \\',
  '  -c file:/opt/aws/amazon-cloudwatch-agent/etc/amazon-cloudwatch-agent.json',
);

Step 6: Install ADOT Python Auto-Instrumentation SDK

Choose based on deployment type identified in "Before You Start".

Option A: Docker Deployment - Modify Dockerfile

For Docker deployments, modify the Dockerfile in the application directory.

1. Install aws-opentelemetry-distro:

Find the line that installs Python dependencies (usually RUN pip install or RUN pip install -r requirements.txt). Add ADOT installation AFTER it:

# Add this line after the existing pip install command
# Use latest version. ServiceEvents requires aws-opentelemetry-distro>=0.18.0.
RUN pip install --no-cache-dir aws-opentelemetry-distro

2. Wrap the CMD with opentelemetry-instrument:

Find the CMD line at the end of the Dockerfile and wrap the command with opentelemetry-instrument:

# Before (Flask):
CMD ["flask", "run"]

# After:
CMD ["opentelemetry-instrument", "flask", "run"]

# Before (any Python app):
CMD ["python", "app.py"]

# After:
CMD ["opentelemetry-instrument", "python", "app.py"]

Django-specific examples:

For Django with Gunicorn (production):

# Before:
CMD ["gunicorn", "-c", "gunicorn.conf.py", "djangoapp.wsgi:application"]

# After:
CMD ["opentelemetry-instrument", "gunicorn", "-c", "gunicorn.conf.py", "djangoapp.wsgi:application"]

For Django development server, add the --noreload flag to prevent auto-reloader conflicts with OpenTelemetry:

# Before:
CMD ["python", "manage.py", "runserver", "0.0.0.0:8000"]

# After:
CMD ["opentelemetry-instrument", "python", "manage.py", "runserver", "0.0.0.0:8000", "--noreload"]

Why modify Dockerfile, not UserData: The ADOT package must be installed inside the container image, not on the EC2 host. UserData commands run on the host and won't affect the containerized application.

Option B: Non-Docker Deployment - Modify UserData

For non-Docker deployments, add to UserData AFTER CloudWatch Agent installation:

instance.userData.addCommands(
  '# Install ADOT Python auto-instrumentation',
  'pip3 install aws-opentelemetry-distro',
);

Step 7: Modify UserData - Configure Application (Docker Deployment)

Only follow this step if you identified Docker deployment in "Before You Start".

Container networking — match the customer's existing setup (minimal change). The example below uses --network host with localhost:4316 endpoints. That pairing is one option, not a hard requirement — the right choice depends on how the container already reaches the host-installed CloudWatch Agent. Don't change the customer's networking model just to instrument; instead pick the variant that fits theirs:

• Already using --network host (or willing to): keep it, and the localhost:4316 / localhost:2000 endpoints in the example work as-is. Trade-off: host networking shares the host's network namespace (no container isolation), though the agent's ports can stay bound to loopback, unreachable off-host. For production, it is recommended to restrict the OTLP 4316 / proxy 2000 ports via EC2 security groups / host firewall and to avoid co-locating untrusted containers; this guide does not apply those controls, so assess and configure them for your environment.
• Using a bridge/default network: don't add --network host. Point the endpoints at the host instead — host.docker.internal:4316/:2000 (add --add-host=host.docker.internal:host-gateway on Linux) or the bridge gateway IP. This requires the CloudWatch Agent to listen on a non-loopback address, so it is recommended to restrict those ports with security groups / host firewall.
• Option 2 — CloudWatch Agent as a sidecar container (most isolated): run the agent as another container on the same user-defined Docker network and target it by name (e.g. cwagent:4316). Nothing binds to host interfaces. This is the same model the ECS guides use; choose it if the customer prefers full container isolation over a host-installed agent.

Step 7A: Base Framework Configuration

Choose the appropriate option based on the framework you identified in Step 3.

Option 1: Standard Python (Flask, FastAPI, Other)

Use this for Flask, FastAPI, or other Python frameworks NOT using Django.

Find the existing docker run command in UserData. Replace it with (this shows the --network host example — adapt per the networking variant you chose above):

instance.userData.addCommands(
  '# Run container with Application Signals environment variables',
  `docker run -d --name {{APP_NAME}} \\`,
  `  -e PORT={{PORT}} \\`,
  `  -e SERVICE_NAME={{SERVICE_NAME}} \\`,
  `  -e OTEL_METRICS_EXPORTER=none \\`,
  `  -e OTEL_LOGS_EXPORTER=none \\`,
  `  -e OTEL_AWS_APPLICATION_SIGNALS_ENABLED=true \\`,
  `  -e OTEL_PYTHON_DISTRO=aws_distro \\`,
  `  -e OTEL_PYTHON_CONFIGURATOR=aws_configurator \\`,
  `  -e OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf \\`,
  `  -e OTEL_TRACES_SAMPLER=xray \\`,
  `  -e OTEL_TRACES_SAMPLER_ARG=endpoint=http://localhost:2000 \\`,
  `  -e OTEL_AWS_APPLICATION_SIGNALS_EXPORTER_ENDPOINT=http://localhost:4316/v1/metrics \\`,
  `  -e OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://localhost:4316/v1/traces \\`,
  `  -e OTEL_RESOURCE_ATTRIBUTES=service.name={{SERVICE_NAME}} \\`,
  `  --network host \\`,
  `  {{IMAGE_URI}}`,
);

Option 2: Django Applications

Use this if you identified Django in Step 3.

Find the existing docker run command in UserData. Replace it with (this shows the --network host example — adapt per the networking variant you chose above):

instance.userData.addCommands(
  `docker run -d --name {{APP_NAME}} \\`,
  `  -e PORT={{PORT}} \\`,
  `  -e SERVICE_NAME={{SERVICE_NAME}} \\`,
  `  -e DJANGO_SETTINGS_MODULE={{DJANGO_SETTINGS_MODULE}} \\`,
  `  -e OTEL_METRICS_EXPORTER=none \\`,
  `  -e OTEL_LOGS_EXPORTER=none \\`,
  `  -e OTEL_AWS_APPLICATION_SIGNALS_ENABLED=true \\`,
  `  -e OTEL_PYTHON_DISTRO=aws_distro \\`,
  `  -e OTEL_PYTHON_CONFIGURATOR=aws_configurator \\`,
  `  -e OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf \\`,
  `  -e OTEL_TRACES_SAMPLER=xray \\`,
  `  -e OTEL_TRACES_SAMPLER_ARG=endpoint=http://localhost:2000 \\`,
  `  -e OTEL_AWS_APPLICATION_SIGNALS_EXPORTER_ENDPOINT=http://localhost:4316/v1/metrics \\`,
  `  -e OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://localhost:4316/v1/traces \\`,
  `  -e OTEL_RESOURCE_ATTRIBUTES=service.name={{SERVICE_NAME}} \\`,
  `  --network host \\`,
  `  {{IMAGE_URI}}`,
);

Step 7B: WSGI Additional Configuration

Only complete this section if you identified a WSGI server (Gunicorn/uWSGI) in Step 3.

If you are using a WSGI server, you must add additional worker instrumentation on top of the configuration from Step 7A.

1. Ensure WSGI configuration file is in the Docker image.

Your Dockerfile must include the appropriate configuration file:

For Gunicorn - Create gunicorn.conf.py:

def post_fork(server, worker):
    from opentelemetry.instrumentation.auto_instrumentation import sitecustomize

For uWSGI - Create or modify uwsgi.ini:

[uwsgi]
enable-threads = true
lazy-apps = true
import = opentelemetry.instrumentation.auto_instrumentation.sitecustomize

2. Add WSGI-specific environment variable to your docker run command.

Go back to the docker run command you configured in Step 7A and add this environment variable:

`  -e OTEL_AWS_PYTHON_DEFER_TO_WORKERS_ENABLED=true \\`,

Add it right after the OTEL_RESOURCE_ATTRIBUTES line and before --network host.

WSGI requirements:

• OTEL_AWS_PYTHON_DEFER_TO_WORKERS_ENABLED=true is REQUIRED for all WSGI servers
• The gunicorn.conf.py or uwsgi.ini file with worker instrumentation is REQUIRED

Step 8: Modify UserData - Configure Application (Non-Docker Deployment)

Only follow this step if you identified non-Docker deployment in "Before You Start".

Step 8A: Base Framework Configuration

Choose the appropriate option based on the framework you identified in Step 3.

Option 1: Standard Python (Flask, FastAPI, Other)

Use this for Flask, FastAPI, or other Python frameworks NOT using Django.

Find the existing command that starts the Python application. Replace it with:

instance.userData.addCommands(
  '# Set OpenTelemetry environment variables',
  'export OTEL_METRICS_EXPORTER=none',
  'export OTEL_LOGS_EXPORTER=none',
  'export OTEL_AWS_APPLICATION_SIGNALS_ENABLED=true',
  'export OTEL_PYTHON_DISTRO=aws_distro',
  'export OTEL_PYTHON_CONFIGURATOR=aws_configurator',
  'export OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf',
  'export OTEL_TRACES_SAMPLER=xray',
  'export OTEL_TRACES_SAMPLER_ARG=endpoint=http://localhost:2000',
  'export OTEL_AWS_APPLICATION_SIGNALS_EXPORTER_ENDPOINT=http://localhost:4316/v1/metrics',
  'export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://localhost:4316/v1/traces',
  'export OTEL_RESOURCE_ATTRIBUTES=service.name={{SERVICE_NAME}}',
  '',
  '# Start application with ADOT instrumentation',
  'cd {{APP_DIR}}',
  'opentelemetry-instrument python {{ENTRY_POINT}}',
);

Option 2: Django Applications

Use this if you identified Django in Step 3.

Find the existing command that starts the Django application. Replace it with:

instance.userData.addCommands(
  'export DJANGO_SETTINGS_MODULE={{DJANGO_SETTINGS_MODULE}}',
  'export OTEL_METRICS_EXPORTER=none',
  'export OTEL_LOGS_EXPORTER=none',
  'export OTEL_AWS_APPLICATION_SIGNALS_ENABLED=true',
  'export OTEL_PYTHON_DISTRO=aws_distro',
  'export OTEL_PYTHON_CONFIGURATOR=aws_configurator',
  'export OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf',
  'export OTEL_TRACES_SAMPLER=xray',
  'export OTEL_TRACES_SAMPLER_ARG=endpoint=http://localhost:2000',
  'export OTEL_AWS_APPLICATION_SIGNALS_EXPORTER_ENDPOINT=http://localhost:4316/v1/metrics',
  'export OTEL_EXPORTER_OTLP_TRACES_ENDPOINT=http://localhost:4316/v1/traces',
  'export OTEL_RESOURCE_ATTRIBUTES=service.name={{SERVICE_NAME}}',
  '',
  '# Start Django application with ADOT instrumentation',
  'cd {{APP_DIR}}',
  'opentelemetry-instrument python manage.py runserver 0.0.0.0:{{PORT}} --noreload',
);

Django-specific notes:

• --noreload flag is REQUIRED to prevent auto-reloader conflicts with OpenTelemetry

Step 8B: WSGI Additional Configuration

Only complete this section if you identified a WSGI server (Gunicorn/uWSGI) in Step 3.

If you are using a WSGI server, you must add additional worker instrumentation on top of the configuration from Step 8A.

1. Ensure WSGI configuration file exists on the EC2 instance.

Your application directory must include the appropriate configuration file:

For Gunicorn - Create gunicorn.conf.py:

def post_fork(server, worker):
    from opentelemetry.instrumentation.auto_instrumentation import sitecustomize

For uWSGI - Create or modify uwsgi.ini:

[uwsgi]
enable-threads = true
lazy-apps = true
import = opentelemetry.instrumentation.auto_instrumentation.sitecustomize

2. Add WSGI-specific environment variable to your configuration.

Go back to the commands you configured in Step 8A and add this environment variable:

'export OTEL_AWS_PYTHON_DEFER_TO_WORKERS_ENABLED=true',

Add it right after the export OTEL_RESOURCE_ATTRIBUTES line.

3. Update the application startup command.

Replace the application startup command with the WSGI server command wrapped with OpenTelemetry instrumentation.

General examples (Flask, FastAPI, etc.):

// Flask with Gunicorn
'opentelemetry-instrument gunicorn -c gunicorn.conf.py app:app',

// Generic Python app with uWSGI
'opentelemetry-instrument uwsgi --ini uwsgi.ini',

Django-specific examples:

For Django with Gunicorn:

// The cd command is from Step 8A, this replaces the startup command
'opentelemetry-instrument gunicorn -c gunicorn.conf.py myproject.wsgi:application',

For Django with uWSGI:

'opentelemetry-instrument uwsgi --ini uwsgi.ini --module myproject.wsgi:application',

WSGI requirements:

• OTEL_AWS_PYTHON_DEFER_TO_WORKERS_ENABLED=true is REQUIRED for all WSGI servers
• The gunicorn.conf.py or uwsgi.ini file with worker instrumentation is REQUIRED
• The startup command must use opentelemetry-instrument wrapper with your WSGI server

Completion

Tell the user:

"I've completed the Application Signals enablement for your Python application. Here's what I modified:

Files Changed:

• IAM role: Added CloudWatchAgentServerPolicy
• UserData: Installed and configured CloudWatch Agent
• UserData: Installed ADOT Python SDK
• UserData/Service file: Added OpenTelemetry environment variables and instrumentation wrapper
• Dockerfile: Installed ADOT Python SDK and modified CMD with instrumentation wrapper (if using Docker)
• WSGI configuration: Added worker instrumentation (if using Gunicorn/uWSGI)

Next Steps:

1. Ensure that Application Signals is enabled in AWS account.
2. Review the changes I made using git diff
3. Deploy your infrastructure:
   • For CDK: cdk deploy
   • For Terraform: terraform apply
   • For CloudFormation: Deploy your stack
4. After deployment, wait 5-10 minutes for telemetry data to start flowing

Verification: Once deployed, you can verify Application Signals is working by:

• Opening the AWS CloudWatch Console
• Navigating to Application Signals → Services
• Looking for your service (named: {{SERVICE_NAME}})
• Checking that traces and metrics are being collected

Monitor Application Health: After enablement, you can monitor your application's operational health using Application Signals dashboards. For more information, see Monitor the operational health of your applications with Application Signals.

Troubleshooting If you encounter any other issues, refer to the CloudWatch APM troubleshooting guide.

Let me know if you'd like me to make any adjustments before you deploy!"