AI Agents

Artificial intelligence (AI) is evolving beyond passive tools into active, autonomous agents capable of perceiving, reasoning, and acting in complex environments. This white paper explores the burgeoning field of AI agents, examining their architecture, capabilities, applications, and the transformative potential they hold for various industries. We delve into the critical challenges and ethical considerations surrounding their development and deployment, and offer insights into the future trajectory of this groundbreaking technology.

1. Introduction:

Traditional AI systems often operate as reactive tools, responding to specific inputs with predefined outputs. AI agents, however, represent a paradigm shift towards proactive, intelligent entities that can:

• Perceive: Gather information from their environment through sensors or data inputs.
• Reason: Process information, make decisions, and plan actions.
• Act: Execute actions to achieve specific goals.
• Learn: Adapt and improve their performance over time through experience.

This autonomy empowers AI agents to tackle complex tasks with minimal human intervention, opening up a realm of possibilities across diverse sectors.

2. Architecture and Capabilities:

The architecture of an AI agent typically comprises several key components:

• Sensors/Data Inputs: Mechanisms for perceiving the environment (e.g., cameras, microphones, APIs).
• Knowledge Base/Memory: Stores information, rules, and learned experiences.
• Reasoning Engine: Processes information and makes decisions (e.g., planning algorithms, machine learning models).
• Actuators/Outputs: Mechanisms for executing actions (e.g., robotic arms, software commands).
• Learning Module: Enables the agent to adapt and improve its performance (e.g., reinforcement learning, deep learning).

Capabilities of AI agents are rapidly expanding, encompassing:

• Natural Language Processing (NLP): Enabling agents to understand and generate human language.
• Computer Vision: Allowing agents to interpret and ¹ analyze visual information.  
  1. www.agentixlabs.com

  www.agentixlabs.com
• Reinforcement Learning: Empowering agents to learn through trial and error in dynamic environments.
• Planning and Reasoning: Enabling agents to strategize and solve complex problems.

3. Applications Across Industries:

AI agents are poised to revolutionize numerous industries:

• Healthcare: Autonomous diagnostic tools, personalized treatment plans, robotic surgery assistants.
• Manufacturing: Intelligent robots for automation, predictive maintenance, supply chain optimization.
• Finance: Algorithmic trading, fraud detection, personalized financial advice.
• Transportation: Self-driving vehicles, autonomous drones, traffic management systems.
• Customer Service: Virtual assistants, chatbots, personalized support.
• Education: Personalized learning platforms, intelligent tutoring systems.
• Entertainment: AI-generated content, interactive gaming experiences.

4. Challenges and Ethical Considerations:

The development and deployment of AI agents present several challenges:

• Explainability and Transparency: Ensuring that agents' decisions are understandable and justifiable.
• Bias and Fairness: Mitigating biases in data and algorithms to prevent discriminatory outcomes.
• Safety and Reliability: Ensuring that agents operate safely and reliably in complex environments.
• Security and Privacy: Protecting sensitive data and preventing malicious use of agents.
• Job Displacement: Addressing the potential impact of automation on the workforce.
• Ethical Guidelines: Establishing clear ethical frameworks for the development and use of AI agents.

5. The Future of AI Agents:

The future of AI agents is bright, with ongoing research pushing the boundaries of their capabilities. We can expect to see:

• Increased Autonomy: Agents becoming more independent and capable of handling complex tasks with minimal human intervention.
• Improved Reasoning and Planning: Agents developing more sophisticated reasoning and planning abilities.
• Enhanced Human-Agent Collaboration: Seamless integration of AI agents into human workflows.
• Emergence of General-Purpose Agents: Agents capable of performing a wide range of tasks across different domains.

6. Conclusion:

AI agents represent a transformative technology with the potential to reshape industries and redefine human-machine interaction. While challenges and ethical considerations remain, the benefits of autonomous AI agents are undeniable. By embracing responsible development and deployment, we can harness the power of AI agents to create a more efficient, productive, and equitable future.

Cannot find JDK classes in '/Library/Java/JavaVirtualMachines/temurin-1

 My micro service upgraded from JDk11 to JDK17. I installed JDK17 ( Both OpenJDK and temurin-17.0.11) in Mac. When I use "Add JDK" in intelliJ it gives me error "Cannot find JDK classes in '/Library/Java/JavaVirtualMachines/temurin-1"

Expectation was JDK should be added successfully

Solution

Go to File > New > Project....

Select Java on the left side, and choose Java SDK 17 as the Project SDK.

Choose the desired project template and settings.

Click Next and follow the wizard to configure your project details.

Click Finish to create the project.

For more details please look https://medium.com/@saijyotshna1999/title-setting-up-java-17-in-intellij-idea-3330b12625d5

Comparison of Various AI Code Generation Tools Available

Introduction

The landscape of software development is undergoing a paradigm shift with the emergence of Generative AI (GenAI) code generation tools. These tools leverage the power of machine learning to automatically generate code, potentially revolutionizing the way software is built. This white paper explores the potential of GenAI in coding, analyzing its impact on developer productivity, code quality, and overall software development workflows.

 

Software development is a complex and time-consuming process, often plagued by bottlenecks and inefficiencies. Developers spend significant time on repetitive tasks like bug fixing, boilerplate code generation, and documentation. GenAI code generation tools offer a compelling solution by automating these tasks, freeing up developers to focus on higher-level problem solving and innovation.

How GenAI Code generation Works ?

GenAI code generation tools are trained on massive datasets of existing codebases, learning the patterns and relationships between code elements. This allows them to statistically predict the most likely code sequence to complete a given task or fulfill a specific function. Users provide prompts or examples, and the tool generates code that aligns with the provided context.

Benefits:

• Increased Developer Productivity: GenAI can automate repetitive tasks, freeing developers to focus on more complex and creative aspects of software development. This can lead to significant time savings and increased output.
• Improved Code Quality: GenAI can generate code that adheres to best practices and coding standards, potentially reducing bugs and improving code maintainability.
• Enhanced Collaboration: GenAI can facilitate collaboration by generating code snippets that fulfill shared objectives, aiding in team development and reducing communication overhead.
• Democratizing Software Development: GenAI has the potential to lower the barrier to entry for software development, making it more accessible to individuals with less technical expertise.

Challenges and Considerations:

While GenAI holds immense promise, it's crucial to acknowledge potential challenges and considerations:

• Limited Creativity: GenAI models are inherently data-driven, potentially limiting their ability to generate truly innovative or groundbreaking code.
• Security Concerns: Malicious actors could potentially exploit GenAI tools to generate harmful code or automate cyberattacks.
• Ethical Implications: Biases present in training data could be reflected in generated code, leading to ethical concerns around fairness and discrimination.
• Job Displacement: Concerns exist around GenAI potentially displacing certain developer roles, necessitating workforce adaptation and reskilling initiatives.

    Major AI Code Generation Tools

      There are various code generation tools are available but the major ones are 

• GitHub Copilot: A popular tool offering code completion and suggestions within various IDEs. This extension for popular IDEs has gained immense popularity due to its seamless integration and wide range of features, including code completion, generation, and translation.
• OpenAI Codex: A powerful code generation model with wide language support and the ability to translate languages and write different kinds of creative content.
• Google AI Codey: A suite of models for code generation, chat assistance, and code completion. This suite of models from Google AI, incorporating PaLM 2, offers code generation, code completion, and natural language assistance, particularly for data science and machine learning tasks.
• Tabnine: An AI-powered code completion tool with language-specific models and cross-language translation capabilities. Known for its speed and language-specific models, Tabnine provides accurate code completion, context-aware suggestions, and the ability to translate between programming languages.
• Ponicode: A tool focused on generating unit tests to ensure code quality. While specialized in generating unit tests for Python code, Ponicode's focus on ensuring code quality makes it a valuable tool for developers aiming to build robust and reliable software.

*Data Collected from google Bard

Estimated Usage Percentage 

These are estimations based on available data and industry insights. Actual usage figures might vary. User adoption within different programming languages and communities can differ significantly. Usage numbers don't necessarily reflect overall tool preference, as developers might use multiple tools interchangeably. The market remains dynamic, and these usage shares could change as new tools emerge and existing ones evolve

Tool	Estimated Usage Share	Notes
GitHub Copilot	40-50%	Largest market share due to IDE integration, active development, and wide user base.
OpenAI Codex	20-30%	Highly accurate and versatile, gaining traction within the developer community.
Tabnine	15-20%	Free-to-use option with strong performance, attracting a loyal user base.
Google AI Codey (Beta)	5-10%	Relatively new, focus on data science/ML tasks holds potential for growth.
Ponicode	<5%	Specialized in unit testing for Python, niche user base but valuable for specific needs.

Programming languages support

All tools support a wide range of popular programming languages. OpenAI Codex offers the most versatility in terms of language support and translation. GitHub Copilot and Tabnine support a broad range of languages but might have limitations with less popular ones. Google AI Codey focuses on data science and ML-related languages. Ponicode is exclusively for Python, but provides deep support for unit testing within that language.

 

Tool	Supported Languages	Notes
GitHub Copilot	Python, JavaScript, TypeScript, Ruby, Java, Go, C++, C#, PHP, Dockerfile, Markdown, and more	Expands support based on user community contributions
OpenAI Codex	Python, JavaScript, Java, Go, C++, C#, shell scripting, SQL, HTML, CSS, and more	Can translate between languages and learn new ones with additional training
Tabnine	Python, JavaScript, Java, Go, C++, C#, PHP, Ruby, Rust, Swift, Kotlin, TypeScript, SQL, HTML, CSS, and more	Offers language-specific models for improved accuracy
Google AI Codey (Beta)	Python, JavaScript, Java, Go, C++, C#, shell scripting, SQL, and more	Focuses on data science and machine learning tasks, supports languages relevant to data analysis
Ponicode	Python (exclusively)	Specializes in generating unit tests for Python code

 

IDE support

As development is going on all types of IDE’s and plugins are not developed for each code generator. GitHub Copilot offers seamless integration with popular IDEs. OpenAI Codex requires specific integration methods but allows customization. Tabnine supports the widest range of editors, promoting flexibility. Google AI Codey is currently limited to Google Cloud Tools and Colab. Ponicode integrates with major Python-focused IDEs.

Tool	Integrated Code Editors	Additional Integration Methods
GitHub Copilot	Visual Studio Code, JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.), Neovim, Visual Studio 2022, Codespaces	None
OpenAI Codex	GitHub Codespaces, JetBrains IDEs (via plugin), Custom integrations via API	Web-based playground for testing
Tabnine	20+ editors including Visual Studio Code, JetBrains IDEs, Vim, Emacs, Sublime Text, Atom, Spyder, Jupyter Notebook, VS Codespaces, and more	Custom integrations via API
Google AI Codey (Beta)	Google Cloud Tools for VS Code, Google Colab	Limited integration with other platforms
Ponicode	Visual Studio Code, PyCharm, and IntelliJ IDEA	None

 

Features support

Different tools supports different features as below. All tools offer code suggestion, function generation, and code completion. OpenAI Codex excels in code translation, explanation, and natural language to code capabilities. Google AI Codey focuses on data science and natural language to code. Ponicode uniquely specializes in unit test generation for Python. Other features like bug detection, code formatting, and refactoring are not widely available yet.

 

Feature	GitHub Copilot	OpenAI Codex	Tabnine	Google AI Codey (Beta)	Ponicode
Code Suggestion	Yes	Yes	Yes	Yes	Yes
Function Generation	Yes	Yes	Yes	Yes	Yes
Code Translation	No	Yes	No	No	No
Code Explanation	No	Yes	No	Limited	No
Code Completion	Yes	Yes	Yes	Yes	Yes
Unit Test Generation	No	No	No	No	Yes
Bug Detection	No	Limited	No	No	No
Code Formatting	No	No	No	No	No
Code Refactoring	No	Limited	No	No	No
Data Science Code	Limited	Limited	Limited	Strong	No
Natural Language to Code	Limited	Strong	Limited	Strong	No

Cost

Individual vs. organization pricing plans often offer different features and usage limits. Some tools require additional costs for integration with specific platforms or services. Free trial periods or limited free plans might be available for some tools. Always check the official website or documentation for the latest pricing information and available plans.

Choosing the best cost option depends on your budget and usage needs whether you are an individual developer or part of an organization and the features and level of support you require.

 

Tool	Individual	Organization	Notes
GitHub Copilot	$10 USD/month, $100 USD/year	Custom pricing available for organizations with 5+ users
OpenAI Codex	Pay-per-use via API calls and resources, or through integration costs (e.g., GitHub Codespaces)	Custom pricing available for enterprise licenses	Requires technical setup and management
Tabnine	Free Basic plan with limited features, Pro plan for $49 USD/year	Custom pricing available for teams with additional features and management options
Google AI Codey (Beta)	Currently in Beta, pricing not yet finalized	Likely tiered pricing models for individuals and organizations based on Google Cloud Tools usage
Ponicode	Free Community plan with limited features, Personal plan for $5 USD/month or $50 USD/year, Professional plan for $25 USD/month or $250 USD/year	Custom pricing available for enterprise licenses with advanced features and integrations

Ease Of Use

GitHub Copilot and Tabnine generally offer the easiest setup and usage. OpenAI Codex provides more flexibility and power, but requires more technical expertise. Google AI Codey's Beta status means its ease of use is still evolving. Ponicode's focus on unit testing for Python makes it easy to adopt for Python developers.

Feature	GitHub Copilot	OpenAI Codex	Tabnine	Google AI Codey (Beta)	Ponicode
Learning Curve	Easy	Moderate	Easy	Moderate	Easy
Configuration	Minimal	High	Minimal	Moderate	Minimal
Integration	Seamless with popular IDEs	Varies (API, Codespaces, custom)	Seamless with most editors	Platform-specific (integrated with Google Cloud Tools)	Integrates with major Python IDEs
Interface	User-friendly and intuitive	Technical and complex	Simple and minimal	Unfamiliar (Beta)	User-friendly and intuitive
Customization	Limited	Extensive	Minimal	Moderate	Limited
Error Handling	Forgiving	Requires user intervention	Forgiving	Beta, error handling not fully tested	Forgiving
Best for Beginners	Yes	No	Yes	No	Yes

 

 

 

GEN AI - Comparing code generator IDEs

 

Comparing AI Code Generator Tools: GitHub Copilot vs. Amazon CodeWhisperer vs. Google AI Codey vs. OpenAI Codex

These four AI code generator tools are revolutionizing software development by providing developers with capabilities to automate repetitive tasks and accelerate their workflow. However, each tool caters to diverse needs and presents unique advantages and limitations. Here's a comprehensive comparison:

Feature	GitHub Copilot	Amazon CodeWhisperer	Google AI Codey	OpenAI Codex
Focus	General-purpose code generation	AWS cloud development	NLP-powered code generation	Open-source code generation model
Supported languages	Extensive (Python, JavaScript, Java, Go, etc.)	Python, Java, JavaScript, TypeScript, C#	Python, Java, Javascript, Go, C++	Various (depending on the application)
IDE integration	Widespread (VSCode, JetBrains IDEs, Neovim, etc.)	Mostly JetBrains and VS Code	Cloud-based platform (Vertex AI)	Requires integration into personal tools
Code generation	Snippets, functions, variable names, etc.	Snippets, functions, entire code blocks	Code from natural language descriptions	Full code generation and code completion
Training data	Open-source code	Open-source and internal AWS code	Large and diverse dataset	Massive open-source code dataset
Customizability	Limited	More customizable	High level of control through API	Highly customizable through code
Security features	Basic security features	Emphasis on security with code analysis and static code scanning	Security provided by Google Cloud Platform	Security depends on the implementation
Target audience	All developers	AWS developers and cloud projects	NLP-focused developers and researchers	Developers and researchers interested in AI code generation
Pricing	Free for individual users with public repositories, paid plans for private repositories	Pay-as-you-go based on usage	Free trials and paid plans	Free for individual use, paid plans for commercial use
Strengths	Seamless integration with GitHub workflow, wide language support, free for individual users	Specialized for AWS, strong security features, more customizable	NLP-based code generation, powerful capabilities, open-source	Open-source model, highly customizable, full code generation
Weaknesses	Limited control over generated code, requires OpenAI API key for private repositories	Limited language support compared to Copilot, pay-as-you-go model	Requires Google Cloud account, less user-friendly interface	Requires technical expertise to use effectively, limited support for specific IDEs or workflows

Choosing the right tool:

The most suitable AI code generator for you depends on your specific requirements and preferences. Here are some key considerations:

• Languages you use: Select a tool that supports the programming languages you need.
• Development environment: Ensure compatibility with your preferred IDE.
• Focus: Choose a tool that aligns with your primary area of development (general-purpose, AWS cloud, NLP, or open-source contribution).
• Customization: Consider the level of control you require over the generated code.
• Security: Evaluate the tool's security features if it's a major concern.
• Budget: Compare the pricing plans and choose the one that fits your needs.

Further insights you see in details:

• GitHub Copilot: Offers a good balance of features and accessibility, ideal for individual developers and open-source projects.
• Amazon CodeWhisperer: Best suited for AWS developers who prioritize security and customization.
• Google AI Codey: Perfect for developers comfortable with NLP and exploring research possibilities.
• OpenAI Codex: Ideal for developers who value open-source tools, highly customizable features, and full code generation capabilities.

Handle Large amount Response Data in Mobile in Web browsers in response.sendRedirect

When we do a redirection from Java servlet through response.sendRedirect with  response size more than 8KB server trims the size to 8K. So it is not possible to send large amount of data .

Generally, we write the code like

String paramValue= "jhghghjghghjghjghjg.........guiusyduiyiusddbmnsa" ;

response.sendRedirect(url+"param="+paramValue) ;

where paramValue > 8KB of parameter name as “param”. "url" is where we want to redirect .

If the above code trims your response size then do the send redirect following ways

Solution -1

So instead of doing redirect at server side send the entire string back to browser so that browser can do a redirect. When browser initiates it does not trim size.

String outstr="<html><script>window.location.href='url?param="+paramValue+"';</script>";

response.getWriter().print(outstr);

Since we return to browser and browser will act based on script.

To avoid cross scripting better to take some protections like below.

String param=request.getParameter("param");

if (param!= null && param.toLowerCase().indexOf("script") != -1 ) {

  return ;

}

This will make sure if the value contains script tag for any malicious use it will not allow to proceed further.

Solution -2

We can also set the high value response directly to session storage from a servlet .

String outstr="<html><script>window.sessionStorage.param= '"+ paramValue+"';url';</script>";

response.getWriter().print(outstr);

Solution 1 will work more in case of mobile apps as mobile apps open browsers in inner browser and they can not read session storage from external browsers where for web both will work.