Building MCP Before MCP Existed: Inside Despegar's Sofia Agent

Building MCP Before MCP Existed: Inside Despegar's Sofia Agent

Author: Demetrios May 8, 2026 Duration: 41:13

Nicolas Alejandro Bogliolo is the AI PM at Despegar, the largest online travel agency in Latin America, and the engineer-product-hybrid behind Sofia, the GenAI travel concierge that beat most of the OTA world to a working multi-agent system.


Before MCP was a standard and before LangChain was widely adopted, his team had already shipped their own orchestration layer and tool protocol in production. This conversation is a rare look at what it takes to build an agentic system that actually books trips, runs on WhatsApp, and keeps adding capabilities without falling over.

Building MCP Before MCP Existed: Inside Despegar's Sofia Agent // MLOps Podcast #375 with Nicolas Alejandro Bogliolo, AI PM at Despegar

What we cover:

- Chappi, the brain of Sofia: how Despegar built an internal orchestration layer when there was nothing off the shelf- Building "MCP before MCP": the custom tool-calling protocol that predated the Anthropic standard- Multi-agent architecture by vertical: flights, hotels, activities, and cars each own their own flow

- Decentralized agent ownership: how any squad in the company can build a flow with central supervision

- Sofia on WhatsApp: making messaging the consumer control center, the way Slack became it for the enterprise

- The five-phase travel arc Sofia covers: dreaming, planning, anticipation, in-trip, and post-trip

- KPI evolution: why "in-scope conversation rate" topped out near 96 percent and what they measure now

- The flight-delay-claim use case and why filing claims through a chatbot is a perfect agent task

- Group trip planning in WhatsApp groups: the next frontier for travel agents

- Sofia as channel of choice: the WeChat-style vision for an agent that handles your entire trip

- Why Despegar held off on giving Sofia the ability to bargain with customers, for now.


Whether you are building production agents, running an OTA, or just curious about how an AI travel concierge actually works under the hood, this episode is full of grounded, in-production lessons from a team that had to invent the patterns the rest of us are now adopting.


Links and Resources:

Despegar: https://www.despegar.com

Sofia announcement: https://investor.despegar.com/news-presentations/news-releases/news-details/2024/Despegar-revolutionizes-the-tourism-industry-introducing-the-regions-first-Generative-AI-Travel-Assistant

Sofia coverage on PhocusWire: https://www.phocuswire.com/despegar-debuts-genai-travel-assistant-remembers-previous-interactions

MLOps Community: https://mlops.community

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Timestamps

[00:00] Sophia Travel Concierge AI

[00:38] Sophia Multi-Agent System

[06:00] AI Limitations in Practice

[13:52] Travel Planning Exploration

[18:03] Group Travel Decision Making

[21:32] Agent Ecosystem Design

[30:14] Sofia's Travel Assistant Vision

[33:35] Orchestration and MCP Design

[40:13] Sophia Negotiation Concerns

[40:47] Wrap up


#AIAgents #MCP #AgenticAI


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
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