Structured Response Planning for Auditable AI Agents
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Solution Overview
Problem
Existing systems for generating natural language responses in computer interactions lack structured planning and interpretability, leading to limited focus and effectiveness in customer interactions.
Innovation Solution
A system that generates structured and auditable responses using a plan, constrained by context-free grammar, involving a plan generator, program generator, and response generator, with auditing to ensure conformity to plan rules and confidence scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a multi-step generation system is used to produce natural language responses, then the system can reason and plan to determine appropriate actions, but the system lacks structured planning and interpretability
Solution Approach 1:
The system segments the response generation process into distinct components: a plan generator that creates structured plans, a program generator that translates plans into executable programs, and a response generator that produces natural language responses. This segmentation provides structured planning and interpretability while maintaining reasoning capabilities.
Solution Approach 2:
The patent introduces an intermediary structured plan as a mediator between the reasoning process and the final response. The plan acts as an intermediate representation that enhances interpretability and structured planning, bridging the gap between multi-step reasoning and actionable responses.
2Adaptability or versatility
If unconstrained generation is used, then the system has flexibility in response generation, but the focus and effectiveness in customer interactions are limited
Solution Approach 1:
The system dynamically adjusts parameters such as temperature and top_k during different stages of generation. Constrained decoding is applied during plan generation to ensure focus and task completion, while allowing more flexibility during creative response formulation, thus balancing effectiveness and adaptability.
3Manufacturing precision
If structured planning with constraints is implemented, then task focus and execution accuracy improve, but system complexity increases
Solution Approach 1:
The system divides complexity into modular components: constraint definition module, plan generation module with constrained decoding, program generation module, and response generation module. Each module handles specific aspects of the task, making the overall system more manageable while maintaining high execution accuracy.
Solution Approach 2:
The system implements feedback mechanisms where generated plans are evaluated against constraints, and confidence scoring is performed to ensure task execution accuracy. This feedback loop maintains precision while managing system complexity through iterative refinement.
Data Source
AI summary
A system that generates an explicit plan to separately guide multiple mechanisms to collectively generate a response to an event. The multiple mechanisms can include a program generation mechanism and a response generation mechanism, each of which may be provided with the generated plan. Each mechanism can utilize a prediction engine, such as for example a machine learning model or large language model, to perform a task based on the generated plan. The event can include an event triggered based on an interaction between an automated agent and a customer. The plan may be generated using a state machine, a machine learning model, such as for example a large language model, or some other mechanism. The plan can be structured such that it can be interpreted and audited for conformance with predefined constraints.