AI Analysis
The package shows minimal risk in terms of network, shell, and obfuscation activities. However, the metadata risk score is elevated due to the newness of the package and the lack of author credibility, raising concerns about potential supply-chain attacks.
- Metadata risk due to new author and incomplete profile
- No direct evidence of malicious activity
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communications.
- Shell: No shell execution patterns detected, indicating the package does not execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
- Metadata: The package is newly created with an author having no history and an incomplete profile, which raises suspicion.
Heuristic Checks
No suspicious network call patterns found
No obfuscation patterns detected
No shell execution patterns detected
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: microsoft.com>
All external links appear legitimate
Repository microsoft/agent-governance-toolkit appears legitimate
3 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a comprehensive agent management system using the 'agent-governance-toolkit-integrations' Python package. This system will serve as a central hub for deploying, monitoring, and managing various AI agents across different platforms such as LangChain, CrewAI, and OpenAI Agents. The application should include the following functionalities: 1. **Agent Deployment**: Allow users to deploy new agents from supported frameworks directly through the application. Users should be able to specify which framework (e.g., LangChain, CrewAI, OpenAI Agents) they want to use and provide necessary configurations. 2. **Agent Monitoring**: Implement real-time monitoring of deployed agents. The application should display key metrics like response time, error rates, and usage statistics for each agent. 3. **Policy Management**: Provide tools for setting up governance policies for the agents. Users should be able to define rules regarding data handling, access control, and performance thresholds. 4. **Alert System**: Set up an alert system that notifies users when an agent fails to meet predefined performance criteria or encounters errors. 5. **Reporting**: Develop a reporting module that generates periodic reports on agent performance and adherence to governance policies. The 'agent-governance-toolkit-integrations' package will be heavily utilized throughout the project. It provides the necessary adapters and utilities to integrate with various agent frameworks seamlessly. Use its features to handle deployment, monitoring, and policy enforcement. Additionally, explore how you can leverage the package's capabilities to enhance the user experience and make the management system more robust and scalable.