Open Source Summit India 2026

AI agents are often limited by proprietary "plugin" silos and bespoke integrations. The Model Context Protocol (MCP) offers a universal interface to break these walls, but moving from local scripts to enterprise-scale introduces Day 2 operational hurdles. How do we discover tool-servers in a dynamic cluster? How do we secure the bridge between LLM reasoning and system execution?

This session presents a technical blueprint for operationalizing MCP within a sovereign, open source stack. We move beyond basic connectivity to address 3 core pillars:
1) Dynamic Discovery: Implementing cloud-native service discovery so agents can dynamically negotiate capabilities with ephemeral MCP servers.
2) Zero-Trust Security: Hardening the "Reasoning-to-Execution" path with fine-grained authorization and audit trails to prevent privilege escalation.
3) Full-Stack Observability: Using OpenTelemetry to bridge the gap between model thinking and backend execution, pinpointing failures in production pipelines.

Attendees will leave with a roadmap for building a modular, interoperable agentic infrastructure, ensuring the future of AI remains open and auditable.

Robust guardrails are a foundational requirement for preparing AI agents for enterprise‑grade deployment. They ensure that agents consistently adhere to organizational standards, policies, and ethical expectations.

OpenTelemetry provides a vendor‑agnostic, standards‑based framework for observing and validating guardrail behavior at runtime. Its unified APIs and emerging generative AI semantic conventions enable organizations to safely instrument guardrail logic, eliminate silent failures, and capture valuable telemetry across decision points.

This session explores the role of guardrails across the entire AI stack, including data layers, infrastructure, orchestration components, and LLMs themselves. We demonstrate how open‑source frameworks such as NVIDIA NeMo Guardrails or OpenLIT can be used to implement production‑ready guardrail mechanisms. Finally, we show how key guardrail metrics such as pass/fail rates, top violation categories, and latency impact can be instrumented using OpenTelemetry and visualized through enterprise observability backends like Dynatrace or Grafana.

You wake up, open X, and see the post: new model just dropped. By the time you finish your coffee, it’s installed, pointed at your repo, and already making “helpful” suggestions. It works. It feels safe. But what did you actually just hand over access to, and how would you even notice if it crossed a line?

In this talk, we’ll unpack why testing every new AI model or coding assistant can quietly introduce serious security risks. Using real examples inspired by incidents like OpenClaw, we’ll show how AI systems can access internal data, influence code paths, and create vulnerabilities without ever asking for permission.

We’ll discuss why experimentation without guardrails can bypass existing security controls and normalize unsafe patterns. Most importantly, we’ll focus on what teams can do instead. The session will include a live walkthrough using the Kubernetes agent-sandbox project to demonstrate how AI agents can be isolated, sandboxed, and constrained with clear boundaries.
By the end, attendees will leave with practical approaches to evaluate AI tools safely, apply guardrails, and enable innovation without putting code or data at risk.

As a developers, we use AI for our tasks. but there are stages.
1. user of web. copy paste code.
2. opencode calude code users basic.
3. using system proimpt PRD driven developement.
4. adding MCP server/ Skills.
5. agentic tools pipelines (code-rabbit)
6. AI Dark factories (gas town)

I have grown from stage 1 to exploring stage 6. creating projects has became trivial with improved models.

Realising tools are there to be tamed but we still need to keep some gaurdrails.

Session will give chronological order how I have grown and How will I develop same project in all stages. using embedded systems example using modbus mcp as well as playwright mcp. in agent.

More over I want to put across the point although AI will make few roles redundant but it will make so many of newer role. How your domain knowledge will help you deliver same projects way faster. passing all minial tasks to AI and perform architectural and review tasks.

Helping AI to write code and Help it to test and fix errors if any. avoiding overfitting.

I will be sharing my personal journey and show How can we achive using FOSS agentic tools for perfect autonomous software facotries.

Predicting how air flows and heat distributes through a physical product is essential for product feasibility studies, performance analysis etc. But it takes hours to days per design variant. You model the geometry, generate a mesh, set up boundary conditions, run the solver, post-process the results. The bigger issue is the back-and-forth between the designer and the simulation engineer. Multiply that by the dozens to hundreds of variants you need to explore a design space, and you get missed deadlines, stale questions, and engineering time spent waiting instead of thinking.
This talk covers usage of OpenFOAM to generate parametric training datasets across geometric and flow parameters with Latin Hypercube Sampling, then train a surrogate model using NVIDIA's PhysicsNeMo framework and its DoMINO (Deep Operator Network for Multi-physics) architecture. DoMINO encodes 3D geometry as a signed distance field, combines it with local surface features, and predicts pressure, temperature, and velocity directly on the part surface in a single forward pass, replacing the entire simulation chain. We'll go through how DoMINO works and why it fits this class of problems.

Learn how to run real AI models fully on-device using Qualcomm’s AI Engine with LiteRT and ExecuTorch. This talk covers architecture, deployment pipelines, performance tuning on NPUs, and real demos showing how to achieve fast, private, low-latency inference on modern AI PCs and mobile devices.