HALL 3 - Tech Talks

As AI agents become more prevalent across industries, ensuring they operate with the depth and accuracy of true subject matter expertise is becoming increasingly important. An expert-first approach focuses on anchoring AI agents in structured domain knowledge, expert insights, and reliable information sources rather than relying solely on generalized model outputs.
This session will explore why grounding AI agents in subject matter knowledge is essential for building trustworthy and effective AI systems. It will discuss the role of expert input, curated knowledge sources, and contextual understanding in enabling agents to deliver more accurate, relevant, and meaningful outcomes. The session will also highlight how organizations can design AI solutions that combine the power of large language models with domain expertise to create more reliable and impactful AI-driven experiences.

Every database architecture decision we have made relied on a few assumptions: callers are predictable, writes are deliberate, connections are short, failures are obvious, and schemas are understood. For decades, these held because a human was always in the loop.

Agentic AI changes that. Autonomous, LLM-powered agents generate queries through reasoning, write at machine speed, hold connections during long chains of thought, fail quietly, and interpret schemas through a model rather than shared context. When we attach them to existing data layers, the assumptions we never formalized start to show their cracks.

This talk walks through those assumptions, the production issues that follow, and how to design databases for agents. Database patterns that once seemed nice-to-have become essential once agents are in the system.

Agentic AI systems are rapidly moving from demos to mission‑critical workflows, but most of them still behave as pattern‑matchers with tools, not as systems that understand cause and effect. The result is familiar: agents that sound confident while suggesting actions that quietly violate business logic, break regulations, or create hidden risk. This talk introduces “causal guardrails”—an architecture where structural causal models (SCMs) sit around GenAI agents to constrain, explain, and validate their decisions. Instead of relying solely on prompts and heuristics, agents must route their plans through explicit causal graphs that encode allowed interventions, downstream impacts, and hard constraints. The session will walk through intuitive examples (credit risk, IT ops, or recommendation workflows), show how to combine LLM-based agents with SCMs in practice, and discuss how this improves robustness, debuggability, and auditability. Attendees will leave with concrete patterns for using causal modeling to keep autonomous GenAI “sane” in real enterprise environments, not just in benchmarks.

The Green Orchestrator proposes a next-generation agentic AI framework designed to coordinate, optimize, and govern distributed energy ecosystems operating at up to 1,000 TWh annual scale. As global energy systems become increasingly decentralized — spanning smart grids, renewable assets, data centers, EV infrastructure, and industrial facilities — existing optimization approaches remain fragmented, reactive, and limited to local objectives. Current AI deployments in energy largely function as advisory tools or isolated predictive models, lacking persistent memory, cross-system coordination, policy-aware autonomy, and multi-objective optimization capabilities.

This proposal introduces a hierarchical, multi-agent orchestration platform built using structured execution graphs (e.g., frameworks such as LangGraph), transforming large language models from conversational systems into goal-directed, stateful decision agents. Unlike conventional AI pipelines, the Green Orchestrator embeds agents within a deterministic, policy-constrained state machine architecture that supports long-horizon reasoning, controlled autonomy, and enterprise-grade observability.

At its core, the platform formalizes each agent as a constrained decision process operating over partially observable system states. Agents maintain belief representations through layered memory architectures consisting of short-term operational context, episodic summaries, and long-term vector-symbolic knowledge graphs. A novel energy-weighted memory optimization mechanism dynamically prioritizes retention based on carbon impact, financial risk exposure, grid stability sensitivity, and regulatory criticality. This approach significantly reduces token overhead while preserving high-value contextual intelligence, enabling scalable deployment across distributed edge environments. The system introduces hierarchical coordination across four layers: global strategic agents, regional grid agents, site-level optimization agents, and asset-level micro agents. Each layer operates within bounded authority while exchanging structured state updates. This creates distributed intelligence with escalation control and conflict resolution mechanisms analogous to enterprise governance structures. Multi-agent interaction is modeled as a stochastic cooperative game with weighted global objectives, enabling simultaneous optimization of energy efficiency, carbon reduction, cost management, resilience, and compliance.

A policy-bound autonomy framework ensures that all agent actions pass through validation gates including regulatory constraint checks, digital twin simulations, and risk evaluation layers before execution. This governance-first design differentiates the platform from experimental agent systems by embedding compliance and safety directly into the decision lifecycle. Domain knowledge is integrated through a hybrid approach combining pretrained model capabilities, retrieval-augmented access to enterprise documentation, structured ontologies of energy assets and constraints, and reinforcement learning via simulation environments. Agents leverage defined tool interfaces — including telemetry APIs, market data feeds, storage dispatch systems, and reporting engines — to interact with operational technology (OT) and enterprise systems in a controlled and auditable manner. The architecture is event-driven, activating agents only when triggered by system changes, thereby reducing computational overhead. Federated edge memory allows localized reasoning while sharing compressed embeddings upward, supporting data sovereignty and low-latency control.

Projected system impact at 1,000 TWh scale indicates that even modest coordinated optimization (8–12%) yields substantial reductions in energy consumption and carbon emissions while improving peak demand management and operational resilience. For enterprises such as Schneider Electric, the platform represents a strategic evolution from intelligent hardware integration to AI-native sustainability orchestration, enabling subscription-based optimization services and defensible intellectual property in policy-aware autonomous control.

In summary, the Green Orchestrator advances the field of agentic AI by integrating hierarchical multi-agent coordination, memory-efficient long-horizon reasoning, policy-embedded governance, and multi-objective optimization within a scalable enterprise framework. It establishes the foundation for a planetary-scale energy nervous system capable of learning, adapting, and autonomously coordinating distributed energy infrastructures responsibly and sustainably.

Agentic AI is reshaping how intelligent systems reason and act — but the next frontier lies in bringing that intelligence into the physical world. This session explores the shift from digital agents to Physical AI systems that interact with real-world environments, devices, and operations. We’ll examine the architectural principles, governance models, and system-level design patterns required to build reliable, scalable intelligent systems beyond the screen.

Most enterprise AI systems stop at generating predictions such as churn probabilities, fraud scores, recommendations, or forecasts, but business value is realized only when those predictions translate into reliable, automated decisions. This session focuses on the critical decision layer that sits between model outputs and real-world enterprise workflows. Designed for developers and ML practitioners, it explores how to build production-ready systems that combine model predictions with rules, thresholds, optimization logic, and human-in-the-loop controls to drive actionable outcomes. The talk will also cover handling uncertainty, edge cases, governance, and monitoring decision quality—not just model accuracy—ensuring AI systems are robust, scalable, and aligned with measurable business impact.

ADM – Founder’s Voice
Redefining the Future of Data with Agentic Intelligence
In this special Founder’s Voice session, Raghu Mitra shares the vision, philosophy, and engineering journey behind Acceldata’s evolution from data observability to Agentic Data Management (ADM). As enterprises scale AI, analytics, and data-driven decision-making, traditional monitoring and reactive governance are no longer sufficient. The future demands systems that do not just observe data—but understand, reason, and act.
ADM represents this next frontier. Built on the foundation of ADOC, ADM introduces autonomous, AI-powered agents that continuously monitor data ecosystems, diagnose issues with contextual intelligence, and execute corrective actions with minimal human intervention. In this session, Raghu explores the architectural thinking, real-world challenges, and breakthrough innovations that shaped ADM into a self-driving data management platform.
Attendees will gain insight into:
The shift from reactive observability to autonomous data operations
How agentic systems transform reliability, quality, and governance
The engineering principles behind scalable AI-native data management
The long-term vision for intelligent, self-healing data ecosystems
This session is not just a product overview—it is a forward-looking perspective on how agentic intelligence will redefine enterprise data strategy in the AI era.

Artificial intelligence is now core infrastructure. Yet many AI workloads operate on shared public platforms, creating exposure risks for proprietary data and strategic intelligence.
Zero-Leak AI Infrastructure delivers dedicated, private AI compute environments built on isolated GPU servers and controlled networking. No public exposure. No uncontrolled egress. No shared tenancy.
Secure AI begins with sovereign infrastructure.

Engineering for Human-Like, Multilingual Voicebots for Bharat explores how voice-first AI systems can be designed to serve India’s linguistically diverse and mobile-first population. Drawing from his experience building large-scale, real-world platforms, Kiran Kumar Katreddi will discuss the engineering foundations behind creating voicebots that feel natural, conversational, and inclusive across multiple Indian languages and dialects. The session will cover how technologies such as speech recognition, natural language understanding, and text-to-speech come together to handle code-mixed speech, regional accents, and low-resource languages, while operating at scale with low latency. Kiran will also highlight the unique challenges and design considerations specific to Bharat, and how human-like multilingual voicebots can significantly expand digital access, improve customer experiences, and enable intuitive interactions for users beyond English-first, text-based interfaces.

Building AI-First Operating Models explores how organizations can move beyond isolated AI initiatives to embed intelligence at the core of their operating model. In this session, Abhishek Singh shares a practical and strategic view on designing AI-first enterprises where data, models, and decision intelligence are tightly integrated into everyday workflows. The discussion will cover the shift from traditional automation to AI-led orchestration, key principles such as data readiness, scalable AI infrastructure, human-in-the-loop governance, and cross-functional collaboration, along with real-world examples of how AI-first models are driving measurable impact across operations, customer experience, and decision-making. Attendees will walk away with a clear framework to transition from experimentation to enterprise-scale AI adoption and build resilient, future-ready operating models.