GoEX: Perspectives and Designs Towards a Runtime for Autonomous LLM Applications

🌈 Abstract

The paper discusses the evolution of Large Language Models (LLMs) from passive information providers to active agents capable of interacting with tools and applications. It highlights the challenges associated with integrating LLMs into existing systems, such as their unpredictability, lack of trust, and difficulty in detecting and mitigating failures in real-time. The paper introduces the concept of "post-facto LLM validation" as an approach to ensuring the safety and reliability of LLM-powered systems, focusing on validating the results of LLM-generated actions rather than the process itself. It also introduces "undo" and "damage confinement" abstractions as mechanisms for mitigating the risk of unintended actions taken in LLM-powered systems. Finally, the paper proposes the Gorilla Execution Engine (GoEx), a runtime designed to enable the autonomous interactions of LLM-powered software systems and agents by safely executing LLM-generated actions and striking a tradeoff between safety and utility.

🙋 Q&A

[01] Evolution of LLM-powered Agents

1. What are the key stages in the evolution of LLM-powered systems?

• LLMs started as chatbots, serving as a bridge for humans to interact with web data in a more intuitive way.
• LLMs then evolved to become active agents capable of executing simple tasks and interacting with applications, services, and APIs.
• The paper envisions a future where LLMs will become deeply integrated into daily workflows and systems, powering personalized systems, hosted agents for enterprises, and third-party integrations.

2. What are the three categories of LLM-powered systems mentioned in the future vision?

1. Personalized LLM-powered workflows for individuals
2. Hosted agents for enterprise and group applications
3. Third-party agents expanding the ecosystem of services

[02] Challenges in Ubiquitous LLM Deployments

1. What are the key challenges identified in integrating LLMs into existing systems?

• Delayed feedback and signals: The lag between LLM actions and feedback complicates error identification and system performance assessment.
• Aggregate signals: The true measure of success often emerges from aggregated outcomes, rather than individual LLM actions.
• The death of unit-testing and integration-testing: The dynamic and unpredictable nature of LLM outputs makes it difficult to establish a fixed suite of tests.
• Variable latency: The auto-regressive nature of LLM text generation can lead to variable inference times, which is a challenge for real-time systems.
• Protecting sensitive data: There is a need to protect user credentials and sensitive data from the LLM, which is an untrusted component.

[03] Designing a Runtime

1. What is the key concept introduced in the paper to address the challenges of LLM-powered systems? The paper introduces the concept of "post-facto LLM validation", which focuses on validating the results of LLM-generated actions rather than the process itself.

2. What are the two abstractions proposed to mitigate the risks associated with post-facto validation?

1. Undo: Allowing users to revert the effects of an LLM-generated output.
2. Damage confinement: Establishing a quantification of the user's risk appetite to bound the potential damage of LLM-generated actions.

3. How does GoEx, the proposed runtime, handle different types of actions generated by LLMs?

• For RESTful API calls, GoEx provides secure authentication and authorization mechanisms, and generates undo actions or enforces damage confinement.
• For database operations, GoEx leverages the transaction semantics offered by databases to provide commit and undo functionality.
• For file system operations, GoEx uses Git versioning to enable undo and damage confinement.