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.
Oct 2026
26 Mon
27 Tue
28 Wed
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31 Sat 11:00 AM – 04:00 PM IST
1 Sun
Submitted Oct 2, 2026
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.
We saw this while building a production multi-agent assistant for enterprise planning. As real conversations grew more complex, ambiguity began to compound across agents and turns. The same entity could acquire different meanings, context could be interpreted differently by different agents, and locally correct decisions could lead to globally inconsistent outcomes.
The root cause wasn’t orchestration. It was semantic drift.
In this talk, I’ll share how we evolved the system around a shared semantic foundation using intent understanding, entity grounding, disambiguation, and explicit semantic state to establish a consistent interpretation before downstream agents act. We also separated semantic state from conversation history and introduced delta-based history management, allowing the system to carry forward what actually changed rather than repeatedly reconstructing meaning from an ever-growing conversation.
Through this evolution, we found that reliability came less from adding more agents or increasingly complex orchestration, and more from establishing clear boundaries between language understanding, semantic state, conversation history, memory management, deterministic logic, orchestration, and agent reasoning.
I’ll walk through the production failures that led us to these changes, the architectural trade-offs involved, and the practical patterns that helped us make the system more reliable under real-world usage.
The central lesson is simple: orchestration can coordinate agents, but it cannot resolve inconsistent meanings. Before agents can collaborate reliably, they need to share an understanding of what the user actually means.
Where appropriate, production examples will be anonymized to focus on the engineering patterns rather than the underlying product or business.
Anyone moving agentic systems from prototype to production and dealing with real-world failures
I’m a Solution Consultant at Sahaj Software. My work spans multi agentic systems and the practical applications of GenAI in engineering.
I enjoy exploring how AI technologies can augment human creativity and decision-making. My research has been presented at the International Conference on Data Analytics and Management, and I’ve spoken at multiple DevDays events and Fifth Elephant conferences, sharing insights on AI agents, AI-assisted software development, and emerging agentic architectures.
https://www.linkedin.com/in/swetha0302
While https://youtu.be/zWWWi5tfkn4?si=SXbpzCFN62GDcAxa was about what are agents and how to build them from scratch, this proposal will be to share the production failures, architectural trade-offs, and practical patterns that transformed our agentic system
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