LG AI Research 的 AAAI 2026 观察：企业如何落地智能体 AI 的设计指南

Jinseok Yang

2026.03.24

4. Demo track insights: Designing observable and reliable agents

While the main track focused on enhancing agents' structural reasoning capabilities, the demo track explored challenges in transforming “the decision-making and execution processes of well-designed agents from a black box into a glass box.”

AgentGraph: A debugging interface that structures the agent execution process into an ontology[3]

AgentGraph demonstrated a tool that makes it possible for humans to understand what the agent actually did. What was impressive in the demo was that it went beyond simply visualizing thousands of lines of execution logs; it restructured the agent, tasks, tools, and data flow into a single graph structure. This allows developers to intuitively grasp, at the architectural level, the decision flow an Agent followed to invoke a specific tool, where it got stuck in a loop, and which steps became bottlenecks. AgentGraph provides a foundation for answering a critical operational question: “Can we trust and safely deploy this agent?”

AgentSeer: Action-level Red Teaming targeting the entire agent execution path[4]

AgentSeer begins with the question: “Can we say an agent system is truly safe just because its final response appears safe?” In environments where multiple agents work in parallel, generating a single response involves sequential steps like memory access, tool calling, and LLM application execution, and memory leaks or unsafe tool usage can occur anywhere along this execution path.

To address these issues, AgentSeer decomposes the agent's execution process chronologically and reconstructs all components into action units. It treats memory access, tool invocation, and LLM application calls as unified action primitives, clearly representing on the graph which component each action affects.

On this graph, AgentSeer performs jailbreak simulations and Red Team payload injections targeting each action and component, step-by-step verifying “what security risks could arise if this agent performed this action at this point.” This demonstrated vulnerabilities along the execution path that were difficult to capture with traditional outcome-driven security testing.

Omega: Converting the agent's choices into natural explanations[5]

Omega is a tool focused on explaining why an agent behaved a certain way in human-understandable language. For systems operating in complex environments, such as robots or logistics agents (for example, in Multi-Agent Path Finding (MAPF) problems) it is difficult to determine the reason for choosing a specific path or action based solely on existing execution logs.

To address this, Omega leverages domain ontologies such as maPO to map complex agent execution logs into an ontology-backed semantic graph. It then interprets the execution path using SPARQL queries and generates natural language explanations with logical reasoning to answer questions like “Why did this agent choose this path?”

Through this approach, Omega demonstrated technology capable of presenting AI judgments in an explainable and verifiable form, even in high-risk operational environments where cause analysis and accountability must be clearly established following an incident.

5. Future Roadmap for the Agent Ecosystem

As demonstrated by numerous studies at AAAI 2026, agents are no longer mere automation tools. What matters now is not just building high-performing AI, but constructing systems that can logically explain how they reached their results, manage resources efficiently, and ensure the entire process is transparently observable. Agent architectures combining these elements will become the standard for enterprise agents going forward.

Agents equipped with ontological structures can evolve into true “AI Co-workers” that independently identify core issues aligned with a company's philosophy and operational context, collaborating with humans through advanced logical reasoning. This represents not mere automation, but a new form of intelligence capable of responsible judgment and trustworthy execution.

As I conclude, I pose one final question:

Will your company continue to entrust the fate of its business to AI within a black box? Or will you seize the future of the autonomous economy through a transparent architecture designed with ontology?

Now is the golden time to move beyond mere adoption and establish a trustworthy agent strategy.

[2] Jilong Lie et al., Debate over Mixed-knowledge: A Robust Multi-Agent Reasoning Framework for Incomplete Knowledge Graph Question Answering, AAAI 2026

[3] Zekun Wu et al., AgentGraph: Trace-to-Graph Platform for Interactive Analysis and Robustness Testing in Agentic AI Systems

[5] Bharath Muppasani et al., OMEGA: An Ontology-Driven Tool for Explaining Multi-Agent Path Finding