AI Interaction Orchestration With Dynamic Plugin Loading
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Solution Overview
Problem
Existing AI applications face challenges in handling incomplete and ambiguous data, leading to inaccurate responses, frustration, and resource wastage, and are inflexible due to reliance on complex BPM processes and rigid workflows, failing to adapt to dynamic user interactions.
Innovation Solution
A system that dynamically loads required functions based on user intent, using AI agents to process messages, identify missing information, and request clarification or additional data through interactive orchestration, leveraging LLMs and API repositories to generate adaptive responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI applications use complex BPM processes and rigid workflows to manage interactions, then process management capability is improved, but flexibility and adaptability deteriorate
Solution Approach 1:
The patent implements dynamic workflow generation where the system adapts processes in real-time based on user input and context rather than following rigid pre-defined BPM workflows. The AI agent dynamically determines which functions to invoke and how to structure interactions, allowing the system to be both reliable through structured processes and flexible through adaptive generation of those processes.
2Speed
If AI applications assume or guess data when uncertain, then response speed is improved, but accuracy deteriorates
Solution Approach 1:
The system uses feedback loops where the AI agent generates hypotheses about user intent, tests them against available data, and refines its understanding based on the results. When data is insufficient, the system requests clarification from the user rather than guessing, ensuring accuracy while maintaining reasonable response times through iterative refinement rather than single-shot assumptions.
Solution Approach 2:
The system performs preliminary analysis of available data before committing to a response path. It pre-identifies what information is missing and prepares clarification requests in advance, allowing it to respond efficiently once the necessary information is obtained rather than stalling when data is incomplete.
3Measurement precision
If AI applications request clarification for incomplete data, then accuracy is improved, but interaction time deteriorates
Solution Approach 1:
The system requests only the minimal necessary information needed to proceed with the user's intent rather than asking for comprehensive clarification on all ambiguities. It prioritizes obtaining critical missing data that would enable a useful response, rather than demanding complete information, thus balancing accuracy with interaction efficiency.
4Adaptability or versatility
If AI applications load all available functions upfront, then function availability is improved, but resource consumption deteriorates
Solution Approach 1:
The system segments its function library into categories and loads only the relevant segments needed for the current user interaction. Rather than having all functions immediately available, it dynamically selects and loads appropriate function groups based on the user's intent and context, reducing memory and computational overhead while maintaining full functionality when needed.
Solution Approach 2:
The system dynamically adjusts its function loading strategy based on the interaction context. Functions are loaded on-demand rather than statically, allowing the system to have high function availability when needed while minimizing resource consumption during idle or simple interactions. The function registry remains available but actual loading is optimized based on current needs.
Data Source
AI summary
Systems and methods for orchestrating interaction with an artificial intelligence (AI) application receive, via an AI agent, a message from a user; modify the message to elicit identification of one or more plugins or functions required to respond; process the modified message in a first AI container to generate a list of the required plugins or functions; load the identified plugins or functions into a second AI container; generate, in the second AI container, an initial computational inference process based on the message and the plugins or functions; determine whether all information required to execute the inference process is available; when all required information is available, execute the inference process, generate a reply, and send the reply to the user via the AI agent; and when information is missing, generate a query requesting the unavailable information and send the query to the user via the AI agent.