Speak at The Fifth Elephant 2026 Annual Conference
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Accepting submissions till 25 Jun 2026, 11:59 PM
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Resources:
- How to create a strong proposal - by Vivek Pemavat, editor of Rootconf Platform Engineering meet-ups: https://hasgeek.com/rootconf/call-for-submissions-platform-engineering-meet-ups/sub/rootconf-platform-engineering-talk-guidelines-MWUtatELnYgQb3dLeU2FA6
Make a submission
Accepting submissions till 25 Jun 2026, 11:59 PM
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sooraj shankar Memory Is a Data System: State, Search, and History for AI AgentsAI agent memory is often discussed as a prompting problem or a vector search problem. In production, it behaves much more like a data system. Agents need to store facts, preferences, decisions, intermediate notes, and shared organizational context. That memory has to be named, scoped, searched, updated, versioned, inspected, and eventually corrected. more
I am submitting for: Track 1 - Data engineering & infrastructure
Type of session: 30 mins talk
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Vivek Sinha High Concurrency & Low Latency Serving on Apache IcebergDescription Everyone is putting their data into Apache Iceberg; almost no one is serving sub-second queries directly from it. Once data lands in Iceberg, a familiar question arises: how do you power real-time experiences without duplicating it in yet another serving system? This challenge is especially sharp in observability workloads like RUM, clickstream, and APM, and in customer-facing analyti… more
I am submitting for: Track 1 - Data engineering & infrastructure
Type of session: 30 mins talk
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Siddhant Agarwal Your AI Agent is lying to you: Observability for LLM systems in productionYou have shipped your LLM-powered agent. Congrats. Now do you actually know what it is doing? Most teams flying blind in production only discover issues when users complain, by which point the damage is already done. This talk dives deep into the observability gap in GenAI systems: why traditional APM tools were never designed for non-deterministic, multi-step agentic workflows, and why bolting t… more
I am submitting for: Track 2 - Building & implementing AI tools & agents in production
Type of session: 30 mins talk
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Karthik Shashidhar How to drive adoption and derive value from your AI-for-data agentDescribe your session in 2 paragraphs Companies are struggling to get value from AI-for-data agents. They have invested considerable time and energy in onboarding such systems but found usage and adoption lacking. The reasons are manifold - business users lack confidence in the systems, providing the right context is a challenge, people simply don’t know what questions to ask. So rather than usin… more
I am submitting for: Track 2 - Building & implementing AI tools & agents in production
Type of session: 30 mins talk
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krishan goyal Optimizing Data Ingestion in Apache PinotOptimizing Data Ingestion in Apache Pinot The Problem more
I am submitting for: Track 1 - Data engineering & infrastructure
Type of session: 30 mins talk
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Pramod Biligiri Editor, The Fifth Elephant 2024 & 2025 editions JK
Jagadish K Editor Session formats at The Fifth Elephant: Birds of Feather sessionsBirds of a Feather (BOF) sessions A Birds of a Feather session is a focused, practitioner-led conversation on a specific topic. It is not a talk, not a panel, and not a product demo. It is a room of people who share a problem, a craft, or a question — and want to think through it together. more
I am submitting for: Track 1 - Data engineering & infrastructure
Type of session: Birds of Feather (BOF) session
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Anand S When Data is for Agents, Not HumansWho will be consuming your data - humans or agents? When it’s agents, do you structure it differently? Optimize for token budgets instead of disk or query cost? more
I am submitting for: Track 1 - Data engineering & infrastructure
Type of session: Hands-on workshop - 2-4 hours
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Anand S Building Verification HarnessesTo confidently deploy in production, you need a robust verification mechanism. Some verification mechanisms are easy. Wrong code doesn’t compile or pass good test cases. Wrong analysis doesn’t meet a post-condition - say a value range or a known aggregate. Wrong proofs don’t validate on LEAN. more
I am submitting for: Track 2 - Building & implementing AI tools & agents in production
Type of session: Hands-on workshop - 2-4 hours
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Abhijith Neerkaje Workshop instructor AI evals workshopOverview Why do Agents make mistakes - 3 Gulfs [Comprehension, Specification and Generalization]. (10 min) more
I am submitting for: Track 2 - Building & implementing AI tools & agents in production
Type of session: Hands-on workshop - 2-4 hours
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Utsab Banerjee Breaking Language Barriers, Not the Bank: Scaling PhonePe’s Aslan to 1.2M Daily QueriesSession Description Traditional keyword search inevitably stumbles when faced with multi-lingual nuances and complex user intent. To solve this at scale, we built and launched Aslan, PhonePe’s natural language search assistant that now seamlessly handles over 1.2 million queries every single day. By breaking language barriers across English, Hindi, Hinglish, Telugu, Bengali, etc Aslan utilizes in… more
I am submitting for: Track 2 - Building & implementing AI tools & agents in production
Type of session: 15 mins talk
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Shivam Gupta Discover Globally, Materialize Locally: Building a Governed Cross-Domain Data Sharing PlatformCross-business-unit data sharing usually starts with good intentions and quickly turns into ticket-driven exports, undocumented copies, governance bottlenecks, and growing compliance risk. At InMobi, multiple business units operate independent lakehouses, catalogs, and data engineering organizations. Combining data across these domains creates significant business value, but traditional approache… more
I am submitting for: Track 1 - Data engineering & infrastructure
Type of session: 30 mins talk
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Pushpendra Singh Chauhan Table-First at InMobi - Migrating a multi-BU data estate to Iceberg "without a big bang"Abstract Most data platforms don’t fail because the tables are wrong — they fail because nobody can find the table, nobody knows who owns it, and every consumer is hard-wired to a physical GCS/blob path that breaks the moment a bucket or partition layout changes. At InMobi, our data landscape grew organically into exactly this: file-and-path datasets with near-zero discoverability, access tightly… more
I am submitting for: Track 1 - Data engineering & infrastructure
Type of session: 30 mins talk
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Aravind Baskaran Self-sufficiency with AIGetting our self-sufficiency up and healthy Self-sufficient systems (and people) are the foundation for us (and any scaleable organisation). Needless to say self-sufficiency is existential for us not just to survive but thrive. Mid-2024 (early AI-wave), we started investing a large share of our time into building the muscle that gets us AI-ready if not AI-native, without hurting our BAU growth. more
I am submitting for: Track 2 - Building & implementing AI tools & agents in production
Type of session: 30 mins talk
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udit Building Reliable Harness for AI AgentsIn an enterprise the hard part is not making the model clever. Modern models already read code and write decent patches. The trouble starts when we hand an agent real work that runs over many steps and many sessions. It drifts, forgets, and often reports that it is done when it is not. Better prompts do not fix this. What fixes it is the environment we build around the model, and that environment… more
I am submitting for: Track 2 - Building & implementing AI tools & agents in production
Type of session: 30 mins talk
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Sanjay Kuniya Defeating the Code Flood: How PhonePe Scales Multi-Agent Loops to Unclog the Dev CycleSession Description As frontier LLMs accelerate code generation across software engineering teams, organizations face a classic realization of Amdahl’s Law. While the code writing phase has been hyper-accelerated, the bottleneck has simply shifted downstream to the sequential, human-in-the-loop code review process. Simply deploying standard AI code review tools or basic chatbot wrappers often exa… more
I am submitting for: Track 2 - Building & implementing AI tools & agents in production
Type of session: 30 mins talk
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Omkar Sabade From Figma Node to Production Code: How we Taught Agents to build UI in PhonePeEvery design system has the same expensive seam: the handoff from Figma to code. A designer ships a pixel-perfect screen, and an engineer spends hours inspecting layout, colors, spacing, and variants, then hand-writing it into the codebase over and over, across every platform the design system targets. At PhonePe we built FigGen, an agentic pipeline that turns a selected Figma node into productio… more
I am submitting for: Track 2 - Building & implementing AI tools & agents in production
Type of session: 15 mins talk
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Dhruv Nigam  Your voice agent is (probably) doomed, OR how not to fall victim to outdated voice agent playbooksGoogly Bhai was busy this IPL season. He live-streamed to an **audience of 300,000 every day **, with 1.5 million minutes of watch time and 1.1 million concurrent viewers at peak. Hundreds of other streamers called him onto their streams to discuss live scores, gossip, and make predictions (see him live jamming with another streamer). He switched to Aussie and British accents mid-conversation at … more
I am submitting for: Track 2 - Building & implementing AI tools & agents in production
Type of session: 30 mins talk
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Hiti Sinha Why Real-Time AI Systems Still Fail: Lessons from Building Decision Pipelines Beyond DashboardsMost enterprise systems today are technically real-time such as, streaming pipelines, event-driven architectures, and low-latency dashboards that are widely adopted. Yet, these systems consistently fail at their primary goal of enabling timely, actionable decisions. In production systems like supply chain and order management platforms, events such as inventory shortages are detected in near real… more
I am submitting for: Track 1 - Data engineering & infrastructure
Type of session: 30 mins talk
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Riya Dhar Too Many Cooks: Production Lessons from Orchestrating Multi-Agent LLM SystemsThe hardest failures in our multi-agent platform didn’t come from the agents - they came from the machinery we built to keep our agents honest. This is a field report from building and operating that orchestration layer in production, where one user-turn fans out across multiple independently deployed agents, gets planned, re-planned, and critiqued before the user sees a token. We’ll set the real… more
I am submitting for: Track 2 - Building & implementing AI tools & agents in production
Type of session: 30 mins talk
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Rajmohan C Diagnosing data pipeline failures with LLM agents: from research to production, and the open challengesModern enterprise data platforms rely on complex data pipelines for data transformation and integration, with thousands running every day to move data across systems. When a data pipeline run fails, the error you see is usually not the root cause. Failures surface far from where they are born, and the component that throws the error is rarely the one at fault; so diagnosing them means reasoning a… more
I am submitting for: Track 2 - Building & implementing AI tools & agents in production
Type of session: 30 mins talk
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