This page is Under Construction while I explore and grow from the experience of an AI Engineer/Product Manager/Business Analyst
Background
My first AI Engineering proof of concept was an exploration of how to think through breaking down a problem into steps that can be automated with traditional logic vs steps that lend themselves to use of an LLM for planning, problem solving, or decisions. This project will build on that experience to explore more complex business needs and objectives.
Note: I am new to the AI Engineering and development journey but I am well versed in IT project planning so I am learning through the experience. Also, this output is AI augmented to accelerate my capability. I am providing the business objectives and asking the exec level questions that guide ChatGPT to refine the output for these sections to assure this will meet my needs.
Objective
The primary goal of this project is to build a coding agent that helps users—both technical and non-technical—turn vague problem statements into clear product requirements and ultimately into functional, secure software that can extend their personal agent's capabilities. The agent will be designed with a set of safety boundaries to ensure the code it generates runs entirely within the user's local system or agentic network. It will have the ability to interact with external services (e.g., retrieving email headers from the user’s email system), but it will not generate or suggest solutions that require the execution of code on external systems to fulfill a user’s objective. If a solution requires hosting code outside of the user’s system, the agent will advise the user to seek external assistance.
The coding agent will:
- Refine problem statements into actionable product requirements through interactive dialogue with the user.
- Build software solutions leveraging open-source libraries and tools, ensuring minimal dependencies and the ability to execute locally.
- Functionally test the generated software to ensure it meets the defined requirements and works as expected.
- Ensure security by integrating security best practices into the generated code and providing guidelines for safe development.
- Prioritize open-source solutions to promote transparency and reuse, reducing the need to build new components unless absolutely necessary.
Furthermore, the safety boundaries will include:
- The agent will only produce code that runs within the user’s local environment and agentic network.
- Critical settings that control the behavior of the agentic network and its tools (e.g., directories, system-level settings) cannot be altered by the agent. For example, the agent will be restricted from modifying system or application settings, ensuring that the system’s core settings remain protected.
This project aims to empower users to build and test their own software solutions securely, privately, and with minimal reliance on external services, while providing a foundation for future tool development within the agentic network.
Motivation & Relevance
In the rapidly evolving landscape of AI and software development, many individuals and small businesses struggle to turn their ideas into functional applications due to a lack of technical expertise or resources. At the same time, most AI solutions available today often rely on third-party cloud services, which may compromise user privacy, control over personal data, and the security of their systems. This project aims to address these issues by providing a coding agent that empowers both technical and non-technical users to build, test, and secure software locally without relying on external services.
The motivation behind this project is to create a tool that:
- Enables non-technical users to turn vague problem statements into clear product requirements, which the agent will then help refine into software that is specifically designed to act as tools for the personal agent. The agent guides users through the development process, ensuring the software built can directly extend or enhance the personal agent’s capabilities.
- Promotes data privacy by ensuring that all generated code and user data stays within the user’s local system, reducing reliance on third-party services or cloud-based solutions.
- Encourages open-source software by prioritizing the use of open libraries and tools, allowing users to build on existing, secure, and well-tested components. This will reduce the need to develop new code from scratch, making the development process more efficient.
- Adheres to strong security practices, with built-in checks to ensure that any generated code meets privacy and security standards, and prevents the agent from modifying system-critical settings.
- Facilitates incremental tool creation by providing a framework where users can build software specifically tailored as tools to enhance and expand the personal agent’s capabilities. The agent can adapt and evolve over time, growing into a more powerful and personalized assistant based on the user’s needs.
By offering this solution, we aim to democratize software development, allowing anyone—regardless of technical skill level—to create secure, functional software that directly enhances their personal agent, while maintaining full control over their systems and data. This project seeks to bridge the gap between technical expertise and everyday problem-solving, providing users with the tools to develop their own solutions securely and privately while enabling them to expand and evolve their personal agent's capabilities as their needs grow.
Project Goals
The primary goal of this project is to deploy a locally-hosted LLM (Large Language Model) alongside a coding agent to empower users in creating software tools specifically for expanding their personal agent's capabilities. This will enable both technical and non-technical users to generate secure, functional tools that integrate seamlessly within their personal agent environment.
- Deploy a Locally-Hosted LLM
- Goal: Set up and optimize a locally-hosted LLM to serve as the core processing engine for the coding agent. This model will handle natural language understanding (NLU), code generation, and problem refinement.
- The LLM can process natural language queries from users, refine vague problem statements, and generate functional code.
- The LLM operates efficiently within the user’s local environment without relying on external cloud services.
- Ensuring the LLM is optimized for local use, considering hardware limitations and resource requirements (e.g., GPU, RAM).
- Selecting the appropriate LLM that balances performance, accuracy, and efficiency without excessive computational costs.
- Develop Coding Agent Infrastructure
- Goal: Build the agent infrastructure that allows users to interact with the LLM to define problems, create product requirements, and generate tools specifically for extending the agent’s capabilities.
- The agent can take user inputs (e.g., vague problem statements) and convert them into structured product requirements.
- The coding agent can generate software that functions as tools to extend the agent’s capabilities and integrates seamlessly into the local system.
- Designing an intuitive user interface that allows non-technical users to effectively communicate their needs.
- Ensuring the agent generates code that adheres to user-defined constraints and system requirements.
- Ensure Security and Privacy in Code Generation
- Goal: Integrate security features to ensure generated code follows best practices for privacy and security, and cannot modify critical system settings.
- All generated code passes security audits and meets privacy guidelines (e.g., GDPR).
- The agent ensures that it cannot modify system-critical settings without explicit user consent.
- Implementing automated security testing and vulnerability checks for the generated code.
- Balancing security with usability, ensuring the user experience isn’t overly restrictive.
- Facilitate Open-Source Integration and Reuse
- Goal: Leverage open-source libraries and components to build the tools, reducing development time and complexity.
- A substantial portion of the generated tools will use pre-existing open-source libraries, reducing the need for custom-built code.
- The generated code adheres to open-source licensing standards and is compatible with other open-source tools.
- Identifying the best open-source libraries that fit the user’s needs, without introducing unnecessary complexity.
- Ensuring compatibility between various open-source tools and maintaining security and performance.
- Create the Framework for Tool Expansion
- Goal: Develop a framework to allow users to build and integrate tools incrementally, expanding their personal agent’s capabilities over time.
- The framework allows users to create, test, and deploy new tools that integrate seamlessly into the personal agent.
- The agent can be extended and adapted without requiring a complete system overhaul.
- Designing a modular architecture that supports incremental expansion and adaptation.
- Ensuring that new tools can be tested and validated easily without disrupting existing functionality.
- Test and Validate the Full Workflow
- Goal: Ensure that the entire workflow—from problem definition, through tool creation, to testing and deployment—works smoothly, providing users with a seamless, functional experience.
- End-to-end testing demonstrates that users can successfully generate tools and deploy them within the agent’s ecosystem.
- The system operates with minimal errors, and the agent meets performance benchmarks.
- Ensuring comprehensive testing that covers edge cases and user-defined requirements.
- Addressing unforeseen bugs or performance issues that may arise during integration and testing.
Key Risks
1. Risk: Limited Resources for Self-Hosting AI Models
Risk Description: Deploying LLMs and associated tools on a limited local environment could result in suboptimal performance, long processing times, or failure due to hardware limitations.
Mitigation Strategy:
- Focus on lightweight, optimized versions of open-source models (e.g., distillation, quantization).
- Prioritize only the most essential models and tools.
- Regularly evaluate resource usage and system performance to identify bottlenecks.
- Consider hybrid cloud options for overflow or backup.
Decision: Proceed with resource-constrained local deployment while continuously evaluating the need for cloud resources on a case-by-case basis. Prioritize tool efficiency and lightweight models.
2. Risk: Integration Complexity of Multiple Platforms (Gmail, LinkedIn)
Risk Description: Integrating with multiple platforms using an open-source agent framework could lead to unforeseen technical difficulties, API limitations, or performance issues.
Mitigation Strategy:
- Break integration into small phases, focusing first on one platform (e.g., start with Gmail before integrating LinkedIn).
- Use robust, well-documented open-source libraries that are community-supported for each platform.
- Design error-handling and fallback strategies in case of integration failure.
Decision: Start with Gmail integration, test thoroughly, then move to LinkedIn. Regularly assess platform-specific challenges before scaling.
3. Risk: Security of Locally Hosted Solutions
Risk Description: Hosting LLMs and email/LinkedIn integration tools locally could expose the system to vulnerabilities or leaks of sensitive data, especially when working with untrusted code or external services.
Mitigation Strategy:
- Implement strong security boundaries within the agentic network (e.g., sandbox execution, strict permission models).
- Ensure all external interactions are carefully monitored and logged.
- Use encryption for sensitive data and implement role-based access controls for system tools.
- Regularly audit and patch security vulnerabilities in self-hosted tools.
Decision: Establish a hardened environment with strict security practices, ensuring no critical system-level settings are alterable by the agent network. Limit external interactions only to necessary data retrieval.
4. Risk: Agent Behavior and Code Safety
Risk Description: The AI agent may generate code that could potentially be harmful or interact with external systems in unintended ways.
Mitigation Strategy:
- Create a safe execution boundary where the agent can only generate code that runs within the local environment.
- Explicitly restrict code execution across external systems unless human intervention is required.
- Regularly update agent models with safety checks to prevent unsafe code generation.
Decision: Enforce strict execution boundaries with the agent network. External calls or code generation requiring external execution should trigger explicit advisories for human intervention.
Work breakdown structure and key questions to ask to address the risks
1. Project Initiation
1.1 Define Requirements
- Subtasks:
- Confirm functional and non-functional requirements.
- Define user stories or use cases for the agent system.
- Establish key success criteria for each feature.
- Key Questions:
- Are the requirements clear and fully scoped, or are there ambiguities?
- Have all stakeholders been involved in defining requirements?
- What are the minimum viable features vs. nice-to-haves?
- Subtasks:
1.2 Risk Assessment and Mitigation Plan
- Subtasks:
- Identify and prioritize key risks (resource, technical, security).
- Develop risk mitigation strategies for each identified risk.
- Key Questions:
- What are the critical risks that could affect the timeline or scope?
- How accurate are the current risk assessments based on available information?
- What contingency buffers should be added for high-risk areas?
- Subtasks:
2. Design & Architecture
2.1 System Architecture
- Subtasks:
- Design overall system architecture (including LLM integration and email/LinkedIn integration).
- Ensure the security model is integrated (e.g., agent network, sandboxing).
- Key Questions:
- How well have we accounted for scalability and performance in the design?
- Are there any unknown dependencies in the system architecture that could delay progress?
- How do we ensure that the architecture allows flexibility for future updates?
- Subtasks:
2.2 Tool Selection & Validation
- Subtasks:
- Select appropriate open-source tools for LLM, email parsing, LinkedIn interaction.
- Validate chosen tools for compatibility, security, and scalability.
- Key Questions:
- Are there any alternative tools that might better meet the project’s needs?
- How do we ensure that the tools are well-supported by the community?
- What happens if the tools become unsupported or fail to meet expectations?
- Subtasks:
3. Development & Integration
3.1 Build Email Parsing Functionality
- Subtasks:
- Develop code for retrieving and parsing Gmail headers.
- Implement logic for processing email data and initiating LLM interactions.
- Key Questions:
- What are the known challenges when interacting with Gmail’s API?
- How do we ensure that the email parsing system scales with larger inboxes?
- What error-handling mechanisms are in place for API failures?
- Subtasks:
3.2 Build LinkedIn Interaction Functionality
- Subtasks:
- Develop code for authenticating and retrieving LinkedIn messages.
- Implement logic for handling LinkedIn data and triggering LLM-based responses.
- Key Questions:
- How do we handle API rate limits and unexpected failures from LinkedIn?
- What security measures ensure LinkedIn data is kept secure?
- Is there a fallback method if the LinkedIn integration fails?
- Subtasks:
3.3 Develop Agent Network and Execution Boundaries
- Subtasks:
- Implement safety boundaries to restrict the agent’s code execution to local resources.
- Implement role-based access control and logging for all agent activities.
- Key Questions:
- How will we enforce strict separation between local execution and external systems?
- How do we log and monitor the agent’s activities to ensure no security breaches?
- Are the safety boundaries flexible enough for future updates without compromising security?
- Subtasks:
4. Testing & Validation
4.1 Unit Testing
- Subtasks:
- Develop unit tests for each module (email parsing, LinkedIn interaction, agent network).
- Automate tests where possible for continuous integration.
- Key Questions:
- Are the unit tests comprehensive enough to cover all edge cases?
- How do we ensure that tests are independent and repeatable?
- What coverage do we have for security-related tests?
- Subtasks:
4.2 Integration Testing
- Subtasks:
- Test integrated system functionality (email + LinkedIn + LLM).
- Conduct end-to-end tests for communication between tools and the agent.
- Key Questions:
- What is the expected behavior when integrating email and LinkedIn features?
- How will we test for latency or downtime from external APIs?
- Are there fallback mechanisms if a feature fails during integration?
- Subtasks:
4.3 Security Testing
- Subtasks:
- Perform penetration testing on agent network boundaries.
- Review all external interactions for vulnerabilities.
- Key Questions:
- What are the top security threats to our self-hosted environment?
- How do we validate the robustness of our sandboxed execution model?
- Have we tested for all common injection or data exfiltration risks?
- Subtasks:
5. Deployment & Monitoring
5.1 Deploy Agent Infrastructure
- Subtasks:
- Deploy the agent on local environment with necessary configurations.
- Implement logging, monitoring, and alerting for system performance and security breaches.
- Key Questions:
- Are deployment pipelines and monitoring systems in place before production launch?
- How will we manage deployment failures and rollbacks?
- What metrics will we monitor to ensure the system operates efficiently?
- Subtasks:
5.2 Continuous Monitoring and Updates
- Subtasks:
- Continuously monitor the performance of the system and interactions with Gmail and LinkedIn.
- Update the system as necessary to improve performance or add new features.
- Key Questions:
- What are the key performance metrics we need to monitor post-deployment?
- How do we plan for scaling the system if needed?
- What’s the process for updating tools or components without disrupting services?
- Subtasks:
6. Documentation & Final Review
6.1 Write Documentation
- Subtasks:
- Document system architecture, codebase, and user manuals.
- Provide detailed instructions for future maintenance or upgrades.
- Key Questions:
- Does the documentation cover all necessary aspects for future developers or users?
- Are there any gaps in documentation that could lead to misunderstandings or errors?
- How will we ensure that the documentation stays up-to-date?
- Subtasks:
6.2 Final Review & Handover
- Subtasks:
- Conduct a final review with stakeholders and end-users.
- Finalize project handover for future maintenance or scaling.
- Key Questions:
- What feedback should be gathered from stakeholders to improve the project?
- How do we ensure that the handover process is smooth and that all team members are aligned?
- What are the next steps post-handover for further project improvements?
- Subtasks:
Key Next Steps to Improve Accuracy to 60%:
- Clarify and Solidify Requirements: Hold deeper discussions with stakeholders to finalize all functional, non-functional, and security requirements.
- Complete Risk Assessment: Review the mitigation strategies and update with more accurate risk impact values.
- Prototype Key Components: Start prototyping critical integrations (email parsing, LinkedIn) to gather more data on time and complexity.
- Refine Time Estimates: Once key components are prototyped, adjust estimates for remaining tasks based on actual performance.
- Iterate on WBS: Keep refining the WBS as you move through each phase, breaking down tasks into more granular steps based on new insights.
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