Theo King — accepted AI-conference papers

2026

AgentGraph: Trace-to-Graph Platform for Interactive Analysis and Robustness Testing in Agentic AI Systems

Zekun Wu, Seonglae Cho, Cristian Enrique Munoz Villalobos, Theo King, Umar Mohammed, Emre Kazim +3

AAAI 2026technical

Modern Agentic AI systems plan, reason, and act across multiple steps, creating execution patterns that are difficult to interpret. Existing observability platforms track prompt I/O and operational metrics but require manual inspection of traces to reconstruct structure and reasoning. We present Age

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2026

AgentSeer: Visualizing and Evaluating Temporal Actions in Agentic AI Systems

Ilham Wicaksono, Zekun Wu, Rahul Patel, Theo King, Adriano Koshiyama, Philip Colin Treleaven

AAAI 2026technical

We present AgentSeer, an interactive observability framework for agentic AI systems. Unlike conventional tracing tools that expose raw spans or model-centric metrics, AgentSeer introduces a dual graph decomposition constructed through a deterministic rule-based parser: a temporal action graph, where

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