LLM Runtime Playbooks for Predictable Complex Task Execution
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Solution Overview
Problem
Standard conversational artificial intelligence (AI) systems lack stability and predictability, hindering their implementation in complex tasks despite the use of large language models (LLMs).
Innovation Solution
A runtime manager controls LLM execution using a 'playbook' of human-readable instructions and structured logical conditions, allowing for varying levels of control and dynamic updates based on user context and remote application interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard conversational AI is used, then the system is simple to operate, but it lacks stability and predictability
Solution Approach 1:
A runtime manager is introduced as an intermediary component between the user and the LLM execution environment. This runtime manager provides structured control over LLM execution through playbooks, managing the complexity internally while presenting a simplified interface to users. The runtime manager handles prompt construction, execution state tracking, and coordinated execution of multiple LLM calls, thereby improving reliability without exposing system complexity to end users.
2Adaptability or versatility
If LLMs are used to complete complex tasks, then task capability is improved, but control and predictability deteriorate
Solution Approach 1:
Complex tasks are segmented into structured playbooks consisting of discrete instructions and conditions. Each playbook represents a self-contained task workflow with defined steps, allowing the system to maintain control over complex operations. The segmentation enables predictable execution by breaking down versatile task capabilities into manageable, controllable units that can be executed sequentially with clear state transitions.
Solution Approach 2:
The runtime manager implements feedback mechanisms by tracking execution states of LLM calls and using this information to dynamically construct subsequent prompts. Execution states are monitored and fed back into the prompt construction process, enabling the system to adapt to LLM responses while maintaining predictable control flow. This feedback loop ensures that versatile task execution remains within bounded, predictable parameters.
3Reliability
If high level of control is applied by runtime manager, then predictability is improved, but execution efficiency deteriorates
Solution Approach 1:
The runtime manager implements dynamic control strategies that adapt the level of management intervention based on task requirements and execution context. For routine, predictable operations, the system allows more autonomous LLM execution to maintain efficiency. For critical or uncertain operations, the runtime manager increases control and monitoring to ensure predictability. This dynamic adjustment of control intensity resolves the contradiction between predictability and execution efficiency.
Data Source
AI summary
Systems and methods are provided that allow for efficient and reliable execution of complex tasks using large language models (LLMs). Disclosed systems may include a runtime manager that is in communication with an LLM, and which controls the LLM's execution of the complex task. This control can be defined by a “playbook” that is provided to the LLM, and which contains a series of instructions that are to be followed by the LLM under a desired level of control by the runtime manager. The playbook can take the form of a script containing a sequence of instructions and related conditions that are written in a human-readable, natural-language format.