HALL 2 - Exclusive Workshops

This talk walks through the real-world journey of building a production-grade Agentic Financial Assistant—from early proof-of-concept experiments to a scalable, reliable system serving real users.

We will explore how large language models evolve into autonomous agents capable of reasoning, retrieving financial context, executing tools, and making safe decisions within enterprise constraints.

The session will cover practical architecture patterns including multi-agent orchestration, tool execution layers, memory and retrieval systems, and LLMOps for monitoring and reliability. We will also discuss the critical production challenges—latency, cost control, hallucination mitigation, guardrails, and evaluation frameworks—and how to design agent systems that are safe enough for financial environments.

Developers will walk away with practical blueprints for building agentic systems that move beyond demos into production.

In an increasingly competitive and data-rich landscape, Consumer Packaged Goods (CPG) companies are investing heavily in analytics, AI, and digital transformation—yet many still struggle to translate insights into impactful business outcomes. This session explores Decision Intelligence as the critical missing link that bridges data, analytics, and real-world decision-making.
We will delve into how Decision Intelligence frameworks enable organizations to move beyond dashboards and predictions toward structured, repeatable, and scalable decision processes. By integrating data, context, human judgment, and AI-driven recommendations, CPG leaders can optimize pricing, promotions, supply chains, and demand planning with greater precision and speed.

Through practical examples and industry use cases, the session will highlight how embedding Decision Intelligence into everyday workflows can unlock measurable improvements in performance, agility, and ROI—helping CPG organizations turn complexity into a competitive advantage.

I’ll share insights on leveraging advanced AI to improve product quality, optimize process efficiency, reduce equipment downtime and increase yield. Looking forward to connecting with the AI developer community and sharing practical perspectives at MLDS 2026.

The rapid expansion of the global space economy is generating unprecedented volumes of data and increasingly complex operational challenges. Traditional AI systems have largely focused on narrow tasks such as image classification or anomaly detection.

However, the next frontier lies in agentic AI systems that can perceive, reason, and act autonomously across multiple components of the space ecosystem.

This talk explores how agentic AI can enable a new generation of autonomous space capabilities, spanning satellite Earth observation, robotic spacecraft operations, and intelligent ground infrastructure.

We will discuss AI agentic workflow examples that detect vehicles and strategic assets from satellite imagery, support autonomous robotic docking and inspection, and optimize ground station networks for efficient satellite communications.

When building Agentic AI systems, decision architecture is not a technical afterthought—it is the foundation of scale, trust, and long-term adoption in real-world environments.

Evolution from conversational bots to action-oriented AI agents
Core agentic patterns (intent → plan → act → observe)
How agents access knowledge and integrate with real-world systems
Agent system architecture and multi-agent collaboration
Challenges, failures, and productionizing agentic AI systems

Interoperability will define the future of agent ecosystems. This workshop unpacks the emerging standards—Model Context Protocol (MCP), Agent-to-Agent (A2A), and Agent Communication Protocol (ACP)—that allow agents to “speak” to each other.

Through hands-on exercises, you’ll compare strengths, trade-offs, and real implementations. You’ll learn to build adaptable systems that can evolve with changing standards—future-proofing your AI stack for a multi-agent, protocol-driven world.

This workshop explores memory architectures that give agents continuity and true persistence. You’ll learn about episodic vs. semantic memory, vector database integration, memory consolidation strategies, and retrieval balancing recency with relevance.

Participants will build agents that learn from every interaction, maintain coherent long-term context, and avoid common pitfalls like context pollution or catastrophic forgetting—core skills for anyone aiming to scale agentic AI responsibly.

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.

Learn how to take agentic applications from lab experiments to production-grade deployments using AWS Copilot. This workshop shows how to automate infrastructure provisioning, CI/CD pipelines, and integrate standards like the Model Context Protocol (MCP) to extend agents beyond chat into real-world tasks.

By the end, you’ll know how to create resilient, scalable, and context-aware AI agents that can truly operate in enterprise environments, while freeing developers to focus on logic instead of infrastructure firefighting.