Transform Custom GPTs and Copilots into Auditable, Enterprise-grade AI Applications
Introduction
In the rapidly evolving landscape of business technology, generative AI tools have emerged as a beacon of innovation, promising to revolutionize how companies operate and engage with their customers. The allure of these tools lies in their ability to create highly efficient and intelligent AI productivity assistants called custom agents. These intelligent AI agents can transform the way businesses handle data, customer interaction, and internal processes. The groundbreaking release of custom GPTs by OpenAI, for instance, has unleashed a wave of potential, enabling users to craft their own generative AI tools through straightforward natural language instructions.
However, as businesses eagerly race to adopt these generative AI tools, they encounter significant roadblocks – concerns regarding safety, security, and governance. These challenges have slowed down the widespread adoption of these tools at an enterprise level. Moreover, the delay in the release of officially sanctioned business tools has led to a proliferation of personal LLM (Large Language Model) tools in the workspace. This development has created a nightmare shadow IT scenario, akin to the Wild West, where unregulated and ungoverned AI tools pose significant compliance and governance challenges.
In this fast-paced environment, a critical question arises: With the ease of creating custom generative AI tools, do businesses still need to collaborate with custom development solutions partners? The answer, perhaps unsurprisingly, is a resounding yes.
The necessity for such partnerships becomes clear when considering the challenges in governance, usage analysis, and integration with existing business tools and knowledge bases – aspects that are often overlooked in the rush to embrace AI's potential.
In this article, we delve into the latest advancements in creating custom generative AI agents using natural language. In doing so, we will explore the nuances of these tools and their transformative impact on personal productivity. However - perhaps more importantly - we will address the significant challenges that come with adopting these tools in an enterprise setting.
These challenges include:
Finally, we will discuss how custom solutions, built upon the foundation of generative AI custom agents, can be seamlessly and successfully integrated into enterprise infrastructures, thereby unlocking their full potential while maintaining the necessary oversight and control.
Join us as we navigate through this exciting yet complex journey of transforming generative AI tools from personal productivity assistants into auditable, enterprise-grade AI applications – a journey that highlights the indispensable role of custom development partners in realizing the true potential of generative AI in the business world.
GPTs and Custom Copilots Unlock Personal Productivity
In an era where digital transformation is pivotal, so-called "custom GPTs" or "copilots" have emerged as key players in enhancing personal productivity. These custom agents, tailored by natural language using advanced AI models, are revolutionizing the way we interact with technology, offering a plethora of functionalities that extend far beyond traditional computing paradigms.
Definition and Capabilities of Custom Agents
Custom agents are essentially AI-driven assistants, developed using nothing but natural langauge and designed to perform a wide range of tasks, from simple data retrieval to complex problem-solving scenarios.
Their multimodal capabilities include, but are not limited to:
ChatGPT and Open AI Custom GPTs
The release of ChatGPT marked a significant milestone in AI development, offering users an interactive and intuitive platform for generating text-based content. Building on this, OpenAI introduced custom GPTs and a dedicated GPT store, enabling users to tailor AI models to their specific needs.
The Introducing GPTs article by OpenAI provides an in-depth overview of the features of the custom GPT solution offered by Open AI. One key feature of this platform is the custom GPT editing interface, showcased in the Open AI Custom GPT Creation Wizard screenshot below, which illustrates the user-friendly nature of customizing AI models using natural language.
OpenAI also introduced ChatGPT for teams , a collaborative platform that allows for the sharing of custom models between team members within an organization, with some of the privacy and security benefits afforded to the enterprise Open AI API. The pricing for these services is detailed on the Open AI ChatGPT Pricing Page .
As of January 2024, the pricing for ChatGPT for teams is set at $25 per user per month.
Microsoft Copilot Pro and Custom Copilots
Parallel to OpenAI's advancements, Microsoft released its Copilot and subsequently, Copilot Pro and Copilot for business. These tools, as announced in their blog post Bringing the full power of Copilot to more people and businesses , are tailored to enhance productivity in various business settings.
Microsoft's Custom Copilots mirror OpenAI's approach but with a focus on business use within the Microsoft 365 ecosystem. They are planning to allow users to create their own Custom Copilots using a new tool called Copilot GPT Builder, adding a layer of customization to the AI assistant experience akin to the Open AI custom GPTs experience.
Microsoft also prioritizes data privacy for its Copilot for business solution, ensuring that company data remains confidential.
Business users who subscribe to Microsoft 365 can try the free version of the Copilot tool by visiting Microsoft copilot . A screenshot of the interface for Copilot is shown below.
Notably, Microsoft also places a strong emphasis on graphic design generative AI solutions, bundling their generative AI image creation tool Microsoft Designer alongside Copilot.
The image and design creation capabilities of this tool can be seen in the Microsoft Designer screenshot below.
As of January 2024, the pricing structure for Microsoft's Copilot services is competitive with Open AI's offerings, with Copilot for Microsoft 365 available at $30 per person per month for small businesses, and Copilot Pro for consumers priced similarly to Open AI's ChatGPT at $20 per month.
Through these developments in GPTs and custom Copilots, the landscape of personal productivity tools is undergoing a significant transformation. The capability to customize AI agents to specific user needs is not only enhancing individual productivity but is also setting the stage for more extensive enterprise-level applications, which, despite their potential, come with their own set of challenges and considerations in a corporate environment. Join us in the next section, where we'll further explore some of the issues and challenges encountered when using custom GPTs and copilots in the enterprise.
Problems with using custom agents in the enterprise
While custom GPTs and Copilots have opened new frontiers in personal productivity and AI integration, their application in the enterprise environment presents unique challenges. Let’s explore some of these key issues.
Prompt Engineering and Output Quality
One of the main challenges with custom agents is the need for prompt engineering – a skill that involves crafting effective and precise prompts to obtain the desired output from the AI. This skill is not only specialized but also somewhat unintuitive, often requiring a steep learning curve. Without proficient prompt engineering, the results produced by custom agents may be suboptimal or irrelevant, leading to inefficiencies in business processes.
Privacy and Data Training Concerns
Depending on the privacy policy of the AI service provider, there's a risk that the AI model may be trained on the data submitted to it. This poses significant concerns for businesses, particularly regarding the confidentiality of sensitive information. Enterprises need to be vigilant about the data they feed into these AI tools to ensure that proprietary and confidential information remains secure.
Usage Analysis and Content Efficacy
Another challenge is the inability to effectively track and analyze the usage of these tools. Enterprises often find it difficult to assess whether the tool is being actively used, whether it's providing helpful content, and what specific content is proving to be most beneficial. This lack of usage analytics can hinder the optimization of these tools for maximum efficiency and effectiveness.
Integration Complexity
Integrating output from multiple AI models or custom agents into existing business workflows can be inefficient and error-prone. This process often relies heavily on manual copy-pasting, which is not only time-consuming but also increases the risk of errors and leaking of sensitive information. Instead, enterprises require seamless integration of AI tools with their existing systems to facilitate smooth and automated workflows.
Security and Compliance Issues
In industries that are heavily regulated, such as healthcare and finance, the use of custom AI agents can raise significant security and compliance issues. Ensuring that these tools adhere to strict regulatory standards and have the proper data guardrails in place is crucial to avoid legal and ethical implications.
Implementation Costs
The cost of deploying these tools can be high, especially considering per-seat pricing models, which typically range from $20 to $40 per user, depending on the service used. For large enterprises with numerous users, this can result in significant expenses.
Difficulty in Integration with Internal Systems
Many enterprises face challenges in integrating custom AI agents with their internal knowledge bases and other data sources. The capabilities of these agents are often limited to the data they can access, and without deep integration with internal systems, their effectiveness is restricted.
Governance and Policy Enforcement
Enforcing corporate governance and usage policies for AI tools across an organization can be daunting. Without proper governance structures in place, there's a risk of misuse or abuse of these technologies, leading to potential harm or reputation damage.
In summary, while custom GPTs and Copilots offer promising prospects for enhancing productivity and AI integration in businesses, their application at the enterprise level is fraught with challenges. These range from technical and operational difficulties to governance and compliance issues. Addressing these challenges is essential for the successful integration and optimization of AI tools in enterprise settings. In the following section, we will explore how transforming these tools into enterprise-grade applications can overcome these challenges.
Transforming GPTs and Custom Copilots into Enterprise-grade tools
The potential of GPTs and custom Copilots in revolutionizing personal productivity is immense, but applying the transformative power of these agents in the workplace necessitates a strategic approach. Upgrading custom GPTs and copilots into business-ready tools involves several critical steps to ensure that these tools are not just innovative but also secure, compliant, and seamlessly integrated into the existing business technology ecosystem.
Exposing Custom Agents as APIs
A pivotal step in transforming custom GPTs into enterprise tools is exposing these custom agents as APIs. This allows businesses to integrate these AI capabilities more deeply into their existing systems and workflows. Through API integration, enterprises can automate tasks, enhance data processing, perform sentiment analysis and categorization, and streamline communication channels, leading to increased efficiency and reduced manual intervention.
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Open AI Assistants API: A Leap Forward in Enterprise LLM Tooling
OpenAI has recently introduced its Assistants API, marking a significant advancement in the realm of enterprise-grade generative AI tooling. This new API heralds a transformative era for developers and businesses, enabling them to create more sophisticated, agent-like AI experiences within their applications.
Innovations in the Assistants API
The Assistants API announced at Open AI Dev Day and detailed in the OpenAI Dev Day Announcement Post , extends beyond the basic generative AI capabilities of ChatGPT. It allows for the creation of purpose-built AIs, or assistants, that are tailored with specific instructions and can access additional knowledge resources. These assistants can also call upon other models and tools to perform a wide array of tasks, effectively reducing the complexity previously encountered in building high-quality, multimodal AI applications.
A significant aspect of this API is its flexibility, catering to a broad spectrum of use cases. From natural language-based data analysis apps and coding assistants to more creative applications like AI-powered vacation planners or voice-controlled DJs, the potential applications are vast and varied. The Assistants API builds upon the capabilities that power OpenAI's new GPTs product, including custom instructions and tools like code interpretation, knowledge retrieval, and custom function calling.
Key Features of the Assistants API
Open AI Assistants API Security and Data Privacy
In line with OpenAI's commitment to security and privacy, data and files passed to the OpenAI Assistants API are never used for model training. Developers retain the freedom to delete data as required, ensuring that sensitive business information remains confidential.
For more information on the security and privacy features of the Assistants API, visit the Open AI Security Page .
Exploring Assistants API Without Coding: The Assistants Playground
OpenAI offers a user-friendly platform for exploring the Assistants API through the Assistants playground, allowing users to experience its capabilities without the need for coding. The playground provides a simple interface for creating assistants, adding instructions, and testing them out. It also offers a variety of pre-built assistants that can be used as templates for creating custom assistants. The Assistants playground is accessible at https://playground.openai.com . Note, an Open AI account is required to access the playground.
Open AI Assistants API Pricing and Accessibility
The Assistants API operates on a 'pay as you go' pricing model, providing cost-effective and scalable solutions for businesses of all sizes. For the most current pricing information and to see how this model can align with your enterprise needs, visit the Open AI Pricing page .
In summary, the Open AI Assistants API represents a significant step forward in safely and securely providing enterprise LLM tooling. Its innovative features, coupled with a strong emphasis on security and data privacy, make it an ideal choice for businesses looking to leverage the power of AI in a controlled and efficient manner.
Leveraging Open-source LangChain Framework for Custom Generative AI Solutions
In the realm of enterprise AI applications, leveraging advanced technologies like OpenAI's Large Language Models and the open-source LangChain framework presents exciting opportunities for businesses to create highly customized and efficient AI solutions. These tools enable the development of sophisticated multi-agent systems that leverage internal company knowledge bases safely and securely, catering to the diverse and evolving needs of modern enterprises.
Here, we delve into two crucial aspects of utilizing these technologies: the integration of custom AI agents with private knowledge bases and internal tools, and the composition of multiple agents to create unique value in the marketplace.
Integrating Custom Agents with Private Knowledge Bases and Internal Tools
To fully harness the power of custom AI agents, integrating them with private knowledge bases and internal tools is essential. This involves using technologies like the open-source LangChain framework to perform Retrieval-Augmented Generation (RAG) with uploaded content, employing custom similarity search retrieval algorithms, and leveraging self-managed vector store databases. Such integration offers finer-grained control over data retrieval and allows for a more tailored and efficient AI response, enhancing the utility of AI tools in complex enterprise scenarios.
Composing Custom Agents for Enhanced Capabilities and Control
The use of individual custom agents for more complex tasks, in which the output from one agent must be processed by another, often leads to cumbersome workflows. These typically involve repetitive tasks such as copying and pasting information from one agent to another, which can be time-consuming and prone to errors. Is there a more streamlined approach to enhancing these workflows?
The answer lies in the composition of multiple agents. By orchestrating a symphony of specialized AI agents, organizations can construct complex, multi-faceted workflows that offer a level of sophistication and control not possible through individual agents. This is another area where LangChain-based custom code solutions come into play, providing the architectural backbone for such an integrated system.
The power of a multi-agent system is in its collaborative efficiency. Each agent, or 'team member,' specializes in a particular function, from research, to web scraping, to writing, to summarization, to image generation. These agents can be directed by a central 'supervisor,' which routes tasks and information between the individual agents, ensuring a seamless workflow. The result is a highly effective robotic team within an AI environment, capable of tackling intricate tasks with precision and speed.
An in-depth exploration of this concept is available in an insightful post on the LangChain blog . This blog post delves further into the mechanics of how different AI agents can be interconnected to function as a cohesive unit, enhancing productivity and output quality.
For a practical example, consider a fully integrated multi-agent "newsroom bot," as depicted in the image below (an illustration from the linked post).
In this setup, the user interacts with a central supervisor agent, which intelligently delegates tasks to various specialized agents. A searcher agent may retrieve relevant information, a web scraper could gather real-time data from various sources, a writer drafts articles, a note-taker organizes the information, and a chart generator creates visual data representations. Each route between the agents represents a communication channel, ensuring the right information reaches the right agent at the right time.
The benefit of such a system is its ability to create unique capabilities that are difficult for competitors to replicate. Unlike basic custom agents that operate in isolation, a well-integrated multi-agent system can tackle complex projects with greater depth and nuance, and it may not be immediately obvious to competitors how the agent is achieving its results. The scalability and adaptability of this approach also mean that as the organization's needs evolve, so too can the system, with new agents being added or existing ones reconfigured to meet changing demands.
In essence, the composition of custom agents into a multi-agent system represents the next evolutionary step in enterprise AI applications. It's a strategy that not only enhances the complexity and quality of the output but also provides a competitive edge in the market.
Economically Deploying LLM Agents at Scale
One of the key advantages of deploying LLM agents through APIs is the 'scale to zero' pricing model. Unlike per-seat pricing, this model is more economical, especially for businesses with a high number of infrequent users.
As seen in the table provided in the image below, the cost of deploying LLM agents through APIs is significantly lower than the per-seat pricing model, in virtually every case, but especially for infrequent users. This makes it a more cost-effective solution over the long term for most businesses, especially those with a large number of users who may only require occasional access to the AI tool.
Implementing Robust Governance for LLM Tool Usage in the Corporate Landscape
Implementing corporate governance and controls is imperative in managing these AI tools. This includes establishing clear usage policies, implementing robust security measures, and ensuring compliance with industry-specific regulations. To accomplish this, we must set up systems for monitoring, logging, auditing, and alerting on any abuse or misuse.
Monitoring and Logging
Clearly, monitoring plays a critical role in this process; logging functionalities allow for the creation of reports that track tool usage, record query types, frequency, user interaction patterns, and response appropriateness. These reports can be analyzed to identify any deviations from set norms or potential breaches, enabling swift action to mitigate potential issues.
Alerting Mechanisms
Alerting mechanisms are vital for timely interventions. These systems should be designed to flag unusual activities, such as atypical usage patterns or content that violates predefined ethical guidelines. Immediate alerts enable swift action to mitigate potential issues, maintaining the integrity and trustworthiness of the AI system.
Analytics: Harnessing AI Insights
Analytics go beyond mere usage statistics. They can delve into the quality and effectiveness of interactions. Indeed, by analyzing aspects like response accuracy, relevance, and user satisfaction, organizations can gauge both 'hot' (frequently accessed) and 'cold' (less used but potentially valuable) knowledge areas. This insight is invaluable for strategic planning of content for inclusion in knowledge bases used for retrieval-augmented generation, and can be instrumental in enhancing user experience over time.
Learning and Evolving: The AI Feedback Loop
By incorporating feedback mechanisms, where high-quality responses are identified through analytics, then integrated into the AI's knowledge base, the AI system is capable of progressively refining its own accuracy and utility. This capacity to perform so-called 'multi-shot' in-context learning ensures that the AI evolves to match the ongoing needs of the organization, shiting as the voice of the organization changes, and matching the brand over time, staying attuned to the dynamic context of the business.
Implementing robust governance for LLM tools is not just about control – it’s about harnessing their full potential responsibly and effectively. As these tools become more integrated into business processes, the need for a well-thought-out governance strategy becomes increasingly critical. It's a journey of continuous improvement, ensuring that LLM tools are not only powerful but also aligned with the ethical and operational standards of the modern business environment.
The Role of Custom Development Partners in Transforming GPTs and Copilots into Enterprise-grade AI Applications
While GPTs and custom Copilots offer immense potential in personal productivity, their transformation into enterprise-grade tools requires a thoughtful approach that addresses security, governance, integration, and scalability. By leveraging APIs like Open AI Assistants, integrating AI tools with internal systems, and applying robust governance, businesses can successfully deploy these cutting-edge technologies, enhancing their operations and gaining a competitive edge in the market.
Custom GPTs and Copilots are invaluable in boosting personal productivity, but their full potential is realized when they are transformed into tools that fit seamlessly within the enterprise infrastructure. This transformation addresses the key challenges of governance, security, integration, and scalability, ensuring that these AI tools not only enhance productivity but also align with enterprise objectives and compliance standards.
The journey from personal AI assistants to enterprise-grade applications is intricate, highlighting the essential role of custom development partners in bridging the gap. These partners offer expertise in prompt engineering, deep integration, and comprehensive governance, ensuring that AI deployments are both effective and compliant.
By partnering with a generative AI solutions expert like Proactive Technology Management's fusion development team, businesses can navigate the complexities of AI adoption at any phase, from initial exploration to full-scale deployment. The fusion development team specializes in crafting tailored AI solutions that align with business goals, integrating cutting-edge AI with existing systems, and implementing robust governance structures.
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VP at KMS Healthcare | Storyteller | Sales and Business Development within Health Technology
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