• The Agent Stack Behind Reliable Agentic Systems 

Agents fail in production for reasons that rarely show up in a demo: lost context, duplicated work, runs that die halfway and start over.

The model isn't the problem. The harness is. The agent harness is the engineering layer wrapped around the model: it plans, fans work out, verifies results, checkpoints.

This session is a working tour of the harness's memory surfaces, the places where memory engineering decides whether an agentic application can be trusted with real work. Reliability lives in this layer, and every one of those capabilities resolves to a memory operation.  progress, and decides what enters the context window.

• Modern AI Agent Memory: Methods, Algorithms, and Challenges 

In this session We will discuss the main challenges that agent memory is designed to address, the algorithmic layer used to improve memory efficiency and coherency, as well asexamples from several leading scientific publications.

We will also explore the key challenges currently facing this field.

• Observability in the AI Era: From Telemetry to Trusted Action 

In this session How AI systems emit richer evidence and AI turns telemetry into operational intelligence