Charlotte Data Science, Machine Learning and AI

As AI evolves from passive tools to active collaborators, enterprises face a pivotal shift: embracing systems that can not only respond but also take initiative. This webinar unpacks what it means to build and deploy agentic AI—models that possess goal-directed reasoning, dynamic planning capabilities, and the ability to autonomously execute tasks across complex workflows.

We’ll explore architectural and governance considerations for enterprise adoption, including how to integrate agentic systems into existing data ecosystems, ensure transparency and control, and maintain human-in-the-loop oversight. Join us for a practical, forward-looking discussion on how to prepare your organization for AI that doesn’t just assist—but acts.

What You'll Learn:

1️⃣ What Agentic AI Really Means: Understand the core components—reasoning, planning, and autonomous action—and how they differ from traditional AI assistants.

2️⃣ Enterprise Readiness: Learn what technical and organizational infrastructure you need to safely and effectively deploy agentic systems.

3️⃣ Designing for Oversight and Control: Explore observability, guardrails, and human-in-the-loop strategies for managing proactive AI in production environments.

Panelists to be announced soon.

Explore the real-world impact of Agentic AI in our fast-paced, insight-packed virtual showcase. Each 10-minute session features a live demo from a leading innovator in the Agentic AI space—highlighting practical applications, dynamic workflows, and tangible business outcomes.

This isn't just another pitch session. It’s a curated look at how agent-based systems are driving intelligent automation, adaptive planning, and cross-functional orchestration across industries. You’ll meet the minds behind these technologies and see firsthand how they’re redefining what's possible in enterprise AI.

What You'll Learn:

1️⃣ Autonomous Workflows: See how agentic systems plan, coordinate, and execute complex tasks across tools and teams.

2️⃣ Dynamic Decisioning: Explore examples of contextual reasoning, goal alignment, and memory-enhanced agents in action.

3️⃣Practical Integration: Understand what it takes to embed agentic AI into your stack—without overhauling everything.

Engage live with founders and builders shaping the future of autonomous enterprise solutions.

You’re short on time but deep on curiosity. This format respects both—cutting straight to the tech and the teams behind it. No filler, no fluff. Just signal.

As AI adoption accelerates, many enterprises are hitting the same wall: traditional approaches to data governance, metadata management, and cataloging were not designed to support AI and AI agents operating at scale. Fragmented tools, siloed semantics, and inconsistent definitions make it difficult for both people and AI systems to trust and act on data.

In this webinar, industry expert Mike Ferguson and Collate co-founder and CEO Suresh Srinivas explore how leading organizations are evolving from tool-centric data catalogs and governance toward semantic intelligence, a unified approach that brings governance, context, and meaning together to support analytics, automation, and AI at scale.

Collate co-founder and CTO Harsha Chintalapani will also provide a brief demonstration showing how semantic intelligence is applied in practice, including how data teams design and build AI agents on governed enterprise data.

What You'll Learn:

1️⃣ Why data discovery, governance, lineage, and data quality and observability must work together to support AI-driven use cases

2️⃣ How fragmented semantic layers and tool-specific context limit trust, scale, and reuse across analytics and AI

3️⃣What semantic intelligence means in practice and how it extends beyond traditional data catalogs

4️⃣How shared meaning enables secure, governed access to data for both people and AI agents

5️⃣How semantic intelligence supports the delivery of trusted, consumable data products with meaning, quality signals, and governance that are ready for analytics and AI agents

6️⃣What it takes to make data truly AI-ready without sacrificing control, compliance, or consistency

Designed for data leaders, enterprise architects, and governance teams, this session explains why existing catalogs and governance tools remain useful—but are no longer sufficient on their own—and how enterprises can operationalize shared meaning to support analytics, data engineering, and AI initiatives at scale.

AI agents are only as effective as the data they can access. Yet most enterprise architectures still depend on batch pipelines, fragmented systems, and tightly controlled operational environments that limit real-time context.

In this technical deep dive, Striim and Google Cloud outline a practical, production-ready architecture for streaming real-time operational data into Google Cloud to power AI at scale.
We’ll examine how low-latency Change Data Capture (CDC) enables continuous movement of trusted enterprise data from on-prem, hybrid, and multi-cloud environments into BigQuery and Cloud Storage—creating a dynamic foundation for analytics, model training, evaluation, and serving on Vertex AI.

The session goes beyond reference diagrams. We’ll explore how continuously refreshed data supports goal-directed reasoning in AI agents, improves memory retention through up-to-date context, and enables dynamic planning in real-world operational workflows. Just as importantly, we’ll address governance, observability, and architectural flexibility—so teams can modernize without compromising compliance or performance.

If you're building AI systems that need fresh enterprise context to act safely and effectively, this session will give you a blueprint grounded in real-world constraints.

What You'll Learn:

1️⃣ Real-Time Data as Agent Context: How low-latency CDC pipelines create continuously updated data products that power training, evaluation, and real-time inference on Vertex AI.

2️⃣ Architecture for Hybrid and Multi-Cloud Enterprises: Patterns for securely streaming operational data from on-prem and distributed systems into BigQuery and Cloud Storage without disrupting core systems.

3️⃣ From Data Movement to AI Activation: How fresh, governed data enables agent memory, dynamic decision-making, and modular AI workflows at scale.

4️⃣ Governance, Observability, and Control: How to maintain compliance, enforce access controls, and monitor data pipelines while supporting production-grade AI systems.

5️⃣Future-Proof Platform Design: How to architect for flexibility—so your AI stack can evolve as models, agents, and enterprise requirements change.

This is an architecture-led session for data and AI leaders who want to move beyond experimentation and build enterprise AI systems that operate in real time—securely, reliably, and at scale.