State and Memory is All You Need for Robust and Reliable AI Agents

TL;DR

This paper introduces SciBORG, a modular AI agent framework that uses finite-state automata memory for robust, reliable, and scalable autonomous scientific workflows, eliminating manual prompt engineering.

Contribution

The paper presents SciBORG, a novel framework combining LLMs with FSA memory for autonomous, context-aware scientific task execution, enhancing robustness and scalability.

Findings

Achieves reliable multi-step scientific task execution.

Enables context-aware decision making and recovery from failures.

Demonstrates effectiveness in physical and virtual hardware applications.

Abstract

Large language models (LLMs) have enabled powerful advances in natural language understanding and generation. Yet their application to complex, real-world scientific workflows remain limited by challenges in memory, planning, and tool integration. Here, we introduce SciBORG (Scientific Bespoke Artificial Intelligence Agents Optimized for Research Goals), a modular agentic framework that allows LLM-based agents to autonomously plan, reason, and achieve robust and reliable domain-specific task execution. Agents are constructed dynamically from source code documentation and augmented with finite-state automata (FSA) memory, enabling persistent state tracking and context-aware decision-making. This approach eliminates the need for manual prompt engineering and allows for robust, scalable deployment across diverse applications via maintaining context across extended workflows and to recover from tool or execution failures. We validate SciBORG through integration with both physical and virtual hardware, such as microwave synthesizers for executing user-specified reactions, with context-aware decision making and demonstrate its use in autonomous multi-step bioassay retrieval from the PubChem database utilizing multi-step planning, reasoning, agent-to-agent communication and coordination for execution of exploratory tasks. Systematic benchmarking shows that SciBORG agents achieve reliable execution, adaptive planning, and interpretable state transitions. Our results show that memory and state awareness are critical enablers of agentic planning and reliability, offering a generalizable foundation for deploying AI agents in complex environments.