# Agentic AI Process Observability: Discovering Behavioral Variability

arXiv: https://arxiv.org/abs/2505.20127 · HTML: https://mlsanity.com/paper/2505.20127/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 26 May 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 6 of 6)

- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [An Empirical Study of the Non-determinism of ChatGPT in Code Generation](https://mlsanity.com/paper/2308.02828/) (2023)
- [AI-Augmented Business Process Management Systems: A Research Manifesto](https://mlsanity.com/paper/2201.12855/) (2022)
- [The WHY in Business Processes: Discovery of Causal Execution Dependencies](https://mlsanity.com/paper/2310.14975/) (2023)
- [The Emergence of Large Language Models in Static Analysis: A First Look through Micro-Benchmarks](https://mlsanity.com/paper/2402.17679/) (2024)
- [Towards a Simple and Extensible Standard for Object-Centric Event Data (OCED) -- Core Model, Design Space, and Lessons Learned](https://mlsanity.com/paper/2410.14495/) (2024)

## Built on by (most cited first, 10 of 10)

- [Taming Uncertainty via Automation: Observing, Analyzing, and Optimizing Agentic AI Systems](https://mlsanity.com/paper/2507.11277/) (2025)
- [Federation of Agents: A Semantics-Aware Communication Fabric for Large-Scale Agentic AI](https://mlsanity.com/paper/2509.20175/) (2025)
- [TraceCompiler: Skill-Guided Mining and Compilation of LLM Agent Traces into Mostly Deterministic Workflows](https://mlsanity.com/paper/2608.02680/) (2026)
- [AI Agents Push Humans Out of the Loop](https://mlsanity.com/paper/2608.23642/) (2026)
- [Agentic Design Patterns: A System-Theoretic Framework](https://mlsanity.com/paper/2601.19752/) (2026)
- [A Research Roadmap for Augmenting Software Engineering Processes and Software Products with Generative AI](https://mlsanity.com/paper/2510.26275/) (2025)
- [Semantic Feature Analysis: Improving Agents Without Searching Over Rollouts](https://mlsanity.com/paper/2604.10513/) (2026)
- [OpenClaw and Ollama in Agentic AI: Toward Fully Autonomous and Scalable AI Agent Systems](https://mlsanity.com/paper/2607.28629/) (2026)
- [Closing the Consistency Gap: Self-Evolving Agents That Learn to Stay on Course](https://mlsanity.com/paper/2609.08832/) (2026)
- [A Hybrid Dependency-Aware Framework for Task Decomposition and Dynamic Agent Generation in Oracle-to-PostgreSQL Migration](https://mlsanity.com/paper/2609.14413/) (2026)

## Abstract (arXiv)

AI agents that leverage Large Language Models (LLMs) are increasingly becoming core building blocks of modern software systems. A wide range of frameworks is now available to support the specification of such applications. These frameworks enable the definition of agent setups using natural language prompting, which specifies the roles, goals, and tools assigned to the various agents involved. Within such setups, agent behavior is non-deterministic for any given input, highlighting the critical need for robust debugging and observability tools. In this work, we explore the use of process and causal discovery applied to agent execution trajectories as a means of enhancing developer observability. This approach aids in monitoring and understanding the emergent variability in agent behavior. Additionally, we complement this with LLM-based static analysis techniques to distinguish between intended and unintended behavioral variability. We argue that such instrumentation is essential for giving developers greater control over evolving specifications and for identifying aspects of functionality that may require more precise and explicit definitions.