1) Native AI Concept
We at BlueCallom created this Native AI concept to leverage the full potential of Agentic AI now and in the Future.
The Native-AI design started by leveraging Natural Language as a dominating factor in sophisticated business solutions. We decided that business solutions should operate autonomously, conduct decision-making, learning, and reasoning at any given point in time, not when the Python code sends a request to an LLM. It should be entirely managed and executed by the intelligence provided by an Agentic AI Infrastructure or AI Operating System.
Moreover, unlike any chatbot, an infrastructure was needed that could support tens or even hundreds of thousands of employees. It would need technology that navigates workflows, data, instructions, and more throughout an AI Network (AIN).
Your most significant benefits of a Native-AI architecture:
- Superior Process Measurability
- Intelligent Workflow Capabilities
- Non-deterministic flow control
- Solution Scalability
- Easy Solution Maintenance
- Rapid Solution Design
- Fast Agent-to-Agent communication and data exchange
- Future AI readiness
NATIVE AI HIERARCHY
- Prompt communication with the LLM, with users, with infrastructure, with environment
- Prompt / LLM process intelligence
- Prompt dominates any code
- Prompt calls Code whenever needed with a function call
- Returns content back to the prompt
2) Native AI Design
‘Intelligence over Code’ Method
ORIGIN
In recent years, AI Agents have been written in Python, a 35-year-old programming language. Our intuition signaled that this was a huge trap. Going back to stiff and linear code would stop truly inteligent application design right there. After some major exploration, we eventually put AI Solution Design on its head – 180°. To fully access the power of agentic AI directly, we decided to expand on the prompt design and structure it in a way that prompts can drive every aspect of a workflow.
BLUEPROMPT
When we talk about PROMPTS, we talk about natural language communication with a GPT/LLM, which is the language the LLM is operating on. However, those prompts are not comparable to the text you enter into a chatbot like ChatGPT. BluePrompts can send emails, access databases, be triggered by events, and have many other features. In other words, BluePrompts are almost like agents. BUT – instead of being written in Python and call an LLM for intelligent responseses, The BluePrompt itself drives and desides what it needs to make intelligent decisions.
THIS IS NOT A ‘NO-CODE SYSTEM’.
Those powerful “BluePrompts” could do things that conventional software could never achieve. In the end, we connect the human brain directly with the artificial brain via those BluePrompts. Note: Native-AI is not a “No-Code” system. Native-AI uses code to enable interaction with external systems, interfaces, and APIs through so-called ‘function calls’. Then, responses and data are processed by the AI. In other words, intelligence dominates the workflow and every aspect of the solution, including function calls. Code is only a subordinate to the intelligent BluePrompts. But that is not all, we needed a robust integration with the environment the AI is acting in – We needed special technology.
Learn more about Intelligence-over-Code here.
3) Native AI Technology
Technology
Instead of a Python code-dominated structure, we build a prompt-dominated structure. Obviously, prompts needed to be far more complex than the one you create for ChatGPT. Prompts in this environment need the ability to send Emails, access Databases, Data lakes, APIs, Interfaces, and more. Also, to learn and improve, the BluePrompts needed to learn and leverage reinforced learning and other learning techniques. Even more importantly, they need to be able to communicate with each other. The technology for all of this was an entire technology suite that works in the dark, invisible to users and even developers. We call this technology suite AgenticBlue. It helps connect all AI Assets (Prmopts, agents, multi-agent applications, clusters, function calls, custom models, and more) through what we call “AgentcSpine“. Our Neuroscience Background came in very handy. We learned countless lessons from nature, like how braincells are connected and control information flow, how the Corpus Callosum operates at quantum speed.
Additional Technological Benefits
- Standardized Agent-to-Agent Protocols
- Synaptic Agent Connectivity
- Agent build Agent Methodology
- Agentic Spine navigation
- Transparent Memory Management
- Easy state management
The AI manages the workflow, interaction, and decisions, handles the reasoning and memory, and even handles code-based features.
We realized that this is finally a Native-AI solution.
The positive impact on developing speed, workflow intelligence in every step, agent-to-agent communication, multi-lateral process design, parallel processing, autonomous feature variety, memory management, and many more aspects is even for us mind-blowing.