Stateful and Responsible AI Agents

Integrating AgentOps Monitoring with Responsible AI Practices

Introduction to AI Agents

The discussion around ChatGPT, has now evolved into AutoGPT. While ChatGPT is primarily a chatbot that can generate text responses, AutoGPT is a more powerful and autonomous AI agent that can execute complex tasks, e.g., make a sale, plan a trip, make a flight booking, book a contractor to do a house job, order a pizza.

Bill Gates recently envisioned a future where we would have an AI agent that is able to process and respond to natural language and accomplish a number of different tasks. Gates used planning a trip as an example.

Ordinarily, this would involve booking your hotel, flights, restaurants, etc. on your own. But an AI agent would be able to use its knowledge of your preferences to book and purchase those things on your behalf.

AI agents [1] follow a long history of research around multi-agent systems (MAS) [2], esp., goal oriented agents [3]. However, designing and deploying AI agents remains challenging in practice. In this article, we focus on primarily two aspects of AI agent platforms:

• given the complex and long-running nature of AI agents, we discuss approaches to ensure a reliable and stateful AI agent execution.

• adding the responsible AI dimension to AI agents. We highlight issues specific to AI agents and propose approaches to establish an integrated AI agent platform governed by responsible AI practices.

Agent AI Platform Reference Architecture

In this section, we focus on identifying the key components of a reference AI agent platform:

• Agent marketplace

• Orchestration layer

• Integration layer

• Shared memory layer

• Governance layer, including explainability, privacy, security, etc.

Fig 1: AI agent platform reference architecture (Image by Author)