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World Congress 2026 Europe • Jul 10, 2026 • Session details

Context Graphs for Explainable, Decision-Aware AI Agents

Zaid Zaim , Jordi Spranger

How do you equip AI agents with the strict rules needed for true explainability? Discover how context graphs weave operational policies into queryable knowledge layers for autonomous robotics.

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#1 about 3 min

Unlocking generative AI capabilities using knowledge graphs

Knowledge graphs provide foundational context and precise tooling to enhance the capabilities of large language models.

#2 about 2 min

Fundamentals of graph databases and semantic relationships

Graphs model real-world relationships by connecting nodes through descriptive edges and key-value properties.

#3 about 4 min

Structuring physical environments through embodied artificial intelligence

Embodied artificial intelligence uses sensors to process visual data and categorize real-world objects into hierarchical semantic relationships.

#4 about 2 min

Overcoming spatial data limits with graph architecture

Graph architectures process spatial relationships in physical spaces efficiently compared to complex relational join tables.

#5 about 3 min

Integrating rules and policies into context graphs

Context graphs extend prompt engineering by embedding operational policies and rules to guide autonomous agent decisions.

#6 about 2 min

Designing short-term and long-term memory for intelligent agents

Agents balance task context utilizing short-term session state alongside long-term environmental observation and explicit reasoning rules.

#7 about 3 min

Defining objects and spatial rules through graph ontologies

Ontologies establish rigorous categorization schemas to populate graph databases mapping objects within literal digital twins.

#8 about 3 min

Optimizing retrieval performance using graph memory as cache

An agented memory service tracks and persists execution traces as workflow graphs to ensure subsequent dynamic queries resolve instantly.

#9 about 2 min

Tracking real-world environmental changes using graph timelines

Comparing sequential digital twin snapshots enables verifiable detection of moved or updated objects within physical spaces.

#10 about 3 min

Embodying artificial intelligence within physical robotic hardware platforms

Physical software development kits expose cloud-scale artificial intelligence models to control mechanical arms and humanoid robots.

#11 about 2 min

Overcoming challenges in adopting organizational context graphs

Providing developers with optimized tools to construct robust schemas is critical for driving broader enterprise adoption of knowledge graphs.

Matching moments

3:30 min

Orchestrating AI agents with a knowledge graph of thought

Gregor Schumacher Gregor Schumacher +2 · WWC 2025

1:15 min

Equipping AI agents with memory and context

Oren Penso Oren Penso · WWC Europe 2026

1:45 min

Overcoming data complexity and context retrieval formatting challenges

Dennis Zielke Dennis Zielke +1 · WWC 2025

3:20 min

Enhancing agent repository context using engineering knowledge graphs

Bastian Heilemann Bastian Heilemann +1 · WWC Europe 2026

4:00 min

Integrating architecture decisions into generative artificial intelligence agents

Vladas Diržys · Europe 2026 Virtual

1:44 min

Introduction to generative AI and knowledge graphs

Michael Hunger Michael Hunger · WWC 2024

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