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UID:pretalx-devconf-us-2026-WZGWBY@pretalx.devconf.info
DTSTART;TZID=EST:20260925T145000
DTEND;TZID=EST:20260925T152500
DESCRIPTION:You are tasked with standing up a large multi-project ecosystem
  that you are not familiar with. What do you do? Do you find yourself stuc
 k reading pages of documentation\, overwhelmed at the idea of understandin
 g and deploying all these new components? Turns out\, learning such system
 s by deploying components one at a time — with an AI partner tracing fai
 lures\, explaining architecture\, and handling mechanical work — can be 
 significantly faster and more effective.\n\nThis talk presents a week-long
  experiment in using AI-assisted collaboration to build a comprehensive un
 derstanding of a Model Context Protocol (MCP) ecosystem spanning ~7 differ
 ent open source projects. The approach followed a deliberate loop: broad q
 uestion\, minimal deploy\, let it fail\, deep trace with the model to unde
 rstand why\, then scope the next increment. The result is a reproducible r
 eference architecture that stands up a full MCP stack in ~10 minutes — c
 atalog-driven server discovery\, lifecycle operator managed deployment\, g
 ateway routing\, Keycloak identity\, per-tool authorization via Kuadrant A
 uthPolicy\, TLS\, Vault-backed credential injection\, and namespace-isolat
 ed multi-tenancy with virtual MCP servers for group-based tool filtering.\
 n\nBeyond the technical aspects\, this talk examines the collaboration pat
 tern itself. AI accelerated breadth — cross-component context\, YAML mec
 hanics\, integration debugging — while the human supplied direction\, sk
 epticism\, and architectural judgment: deciding when to push back on over-
 engineering\, when a workaround was acceptable versus when it needed upstr
 eaming\, and where to draw the boundary between temporary scaffolding and 
 lasting patterns.\n\nAttendees will come away with practical insights on u
 sing AI as a learning partner for complex integration problems and a persp
 ective on how critical thinking changes shape\, but doesn't necessarily di
 sappear when AI enters the workflow.
DTSTAMP:20260727T175211Z
LOCATION:Ladd Room (Capacity 170)
SUMMARY:Empty cluster to a production-shaped MCP Ecosystem - Using AI as a 
 Learning Accelerator - Jaideep Rao
URL:https://pretalx.devconf.info/devconf-us-2026/talk/WZGWBY/
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