Day 1

The talk introduces an innovative framework for training AI agents using trajectory deep reinforcement learning (GRPO) and active intelligent memory utilization. By treating entire decision trajectories as the core unit of learning, it addresses challenges such as trajectory blindness and high supervision costs, thereby enhancing agent performance and understanding. This framework supports continuous experiential learning without extensive human oversight and incorporates a modular evaluation methodology for assessing enterprise agentic platforms. The framework aims to improve the efficiency, adaptability, and risk management of AI systems, driving wider adoption in enterprise environments.

The tacit premise of modern ML: decision-time computation is where value gets created. Train a model, learn a policy, deploy. The policy is the intelligence. In this talk I will demonstrate that structural graph design, specifically via randomised edge perturbation achieves Pareto-dominant performance over learned and optimisation-based methods in urban food delivery. Our core claim is that-if the environment in which agents operate is designed with sufficient structural care, the agents themselves can be remarkably simple; and the system as a whole will still outperform agents that are far more computationally sophisticated.

Enterprise support delegation and resolution is complex: heterogeneous workflows, partial observability, high-risk actions, and strict policy constraints. We present an agentic Supervisor + Specialists architecture that maintains a shared ticket state, generates hierarchical plans via plan graphs, replans using validation signals, and orchestrates multiple skills, with pre/postcondition checks as verifiers via a custom agent harness.

The hospitality industry thrives on trust, experience, and word-of-mouth advocacy. This session explores how an AI-based referral engine can transform customer referrals from a reactive process into a scalable, intelligence-driven growth lever.

The leader will share how AI can be used to identify customers with a high propensity to refer, enabling businesses to focus efforts where advocacy is most likely to convert. The session will also cover smart allocation of relationship managers using predictive insights, ensuring high-value interactions are prioritized and resources are optimally deployed.

Additionally, the talk will highlight how AI-driven profile enrichment—combining behavioral, transactional, and engagement data—can power deeply personalized communication at scale. Attendees will gain practical insights into building referral ecosystems that are proactive, personalized, and measurable, driving sustainable growth and stronger customer relationships in the hospitality sector.

Building machine learning models is only a small part of the challenge in heavy-industry environments. The real complexity lies in deploying, scaling, and operating ML systems that must work reliably on the shop floor—often under strict safety, latency, and reliability constraints.

This session walks through the end-to-end journey of building production-grade ML systems for heavy industry, covering data acquisition from industrial systems, model development, validation, and deployment into real-world decision workflows. It will highlight how ML models are integrated with existing operational technology (OT) systems, how predictions translate into actionable shop-floor decisions, and how teams handle issues like data drift, model monitoring, explainability, and human-in-the-loop controls.

Attendees will gain practical insights into ML system design, MLOps, and decision engineering in industrial settings, along with lessons learned from taking models out of notebooks and into mission-critical production environments.

Agentic systems are non-deterministic—making them harder to debug with traditional logs. This workshop takes you deep into telemetry: instrumentation, observability pipelines, and analysis techniques that capture reasoning loops, tool failures, and system context.

You’ll walk away with hands-on methods to turn raw signals into actionable insights, ensuring your autonomous agents remain reliable and explainable in production, even when facing unpredictable environments.