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June 9-10, 2026
Bengaluru, India
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IMPORTANT NOTE: Timing of sessions and room locations are subject to change.


Wednesday June 10, 2026 12:15pm - 12:40pm IST
At NetoAI we build AI agents for telecom network operations. Our Rapid Root Cause Analysis Agent, built on our open-source TSLAM models (22k+ HF downloads), runs against live operator networks.

When we moved to MCP as the tool-interface layer, agents that passed eval started breaking weeks after production launch. The model, prompts, retrieval, none were root cause. The tool environment itself was.

So we built a digital-twin simulation of our production telecom domain and stress-tested MCP agents across four axes:
1). tool-set scale
2). task complexity
3). persona variability
4). deterministic repetition.

Tool-selection accuracy is near-perfect up to ~20 exposed tools, then collapses. One bad early dependency step cascades the whole workflow.

I'll walk through the seven failure patterns we kept hitting, including Tool Selection Collapse and Cascading Fragility, the three architectural root causes behind them, and the task-scoping and dependency-aware fixes that worked. You leave with a pre-launch methodology for your own MCP servers vendor-neutral, applicable to any dependency-dense domain.
Speakers
avatar for Divya Vijay

Divya Vijay

Senior AI Engineer, NetoAI
Senior AI Engineer at NetoAI, building production agent systems. Co-author on T-VEC and G-SPEC research papers. Previously Software Engineer at Prodapt. 3 years of shipping AI and full-stack software across agentic and UI engineering in telecom.
Wednesday June 10, 2026 12:15pm - 12:40pm IST
Scarlet 2&3

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