Enterprise AI in Production: Mumbai Call for Proposals
Share what it takes to run AI inside an enterprise: architectures, trade-offs, and lessons from production.
Accepting submissions till 11 Oct 2026, 11:59 PM
Not accepting submissions
What to include in your submission
- Title and one-line pitch: what will the audience learn?
- Abstract, 200–400 words: the problem, implementation, key decisions, and outcome.
- Architecture summary: components, data flows, models, and tools. A diagram is welcome.
- Production evidence: scale, time in production, measured results, and how you measured them. State where figures are approximate or anonymised.
- Failures and trade-offs: at least one thing that went wrong or that you would do differently.
- Three takeaways: specific things attendees can apply.
- Audience and prerequisites: who the session is for and what they should already know.
- Preferred format: 30-minute talk or 45-minute deep dive, including questions.
- Speaker details: name, role, organisation, short biography, contact details, and a professional profile. Prior talks or writing are optional.
- Disclosures and availability: commercial interests, confidentiality constraints, and your availability to speak in Mumbai on 31 October and take part in editorial preparation.
Draft slides or a short recorded walkthrough are optional.
Make a submission
Accepting submissions till 11 Oct 2026, 11:59 PM
Swetha A When Good Agents Disagreed Until We Built Shared UnderstandingAbstract Multi-agent systems are often built around orchestration: an orchestrator routes requests to specialist agents, coordinates their work, and brings their outputs together. But in production, we encountered a more fundamental problem: every agent could be doing its job correctly, while the system as a whole was still wrong. more
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Swetha A Understanding Your Agents From Traces to TrustAbstract While building and operating LLM-powered systems in production, one lesson keeps repeating itself which is that the hardest failures are often invisible. Unlike traditional software, agentic systems can fail in countless ways while every dashboard remains green. An agent may misunderstand user intent, choose the wrong tool, retrieve stale information, drift from its objective, or silentl… more
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Manas Chaturvedi From AI Agents to AI Platform: Lessons From Building an In-House Multi-Agent PlatformFrom AI Agents to AI Platform: Lessons From Building an In-House Multi-Agent Platform more
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Akash raj From pilot to production: rolling out an AI code intelligence platform inside a global enterpriseFrom pilot to production: rolling out an AI code intelligence platform inside a global enterprise. What it actually takes to get an AI tool past identity, security and audit, and into the hands of a real engineering team. more
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Uzair Ali Engineering Trust in a Financial Agent: The Harness Behind the ChatEngineering Trust in a Financial Agent: The Harness Behind the Chat more
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Mahita D The Hard Part Isn't Building an Agent. It's Changing One That's Already in Production.What happens when the underlying API that your agentic system relies on changes unexpectedly? This talk is a production case study of how I handled an upstream API change in two weeks for a system already running in production, without regressions. more
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