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Agents in the Enterprise with MongoDB | Interrupt 26

LangChain16 June 2026Watch on YouTube

Part of series

Ep. 4 · LangSmith Engine: Agents bouwen

LangChain laat zien hoe LangSmith Engine het evalueren en verbeteren van AI-agents automatiseert via trace-analyse.

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What you'll learn

  • You learn why enterprise AI is all about unstructured data and how MongoDB addresses this with native search, vector search and embeddings.
  • You see how different customer types, from frontier labs and AI-native companies to enterprise customers, use MongoDB for AI, including the ElevenLabs case study.
  • You discover the three-legged stool of an agent stack, LLM, data and harness layer, and how LangChain and MongoDB work together in this.
  • You hear about the three buckets of agents, employee, partner and customer-facing, and why customer-facing agents are the real prize.
  • You gain insight into real blockers such as stack paralysis, regulation and evals, plus MongoDB's internal stats where 70% of code is written by AI.

Frequently asked questions

Why was 2025 expected to be the year of agents but did not deliver?
CJ Desai and Harrison Chase discuss that predictions of the year of agents did not materialize due to real blockers such as stack choice, regulation and evals, a phenomenon they call agent stack paralysis. The result is that 2026 feels more stable than 2025.
Why can real-time agents not be built on data warehouses?
According to the discussion between CJ Desai and Harrison Chase, real-time agents cannot be built on data warehouses because context and memory keep growing and therefore require a system of intelligence. They call this the system of intelligence concept, where context and memory become bigger than traditional data platforms.
Which three buckets of agents are distinguished?
The three buckets of agents that are distinguished are employee agents, partner agents and customer-facing agents. According to the speakers, customer-facing agents are the real prize because they represent the greatest value for companies.
What percentage of code is written by AI at MongoDB?
MongoDB's internal stats show that 70% of code is written by AI. This insight is linked in the conversation to token costs and the ROI of coding agents, including the human versus tool cost equation.

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Description from the channel

Harrison Chase sits down with CJ Desai, CEO of MongoDB, for a fireside chat at Interrupt 2026, the agent conference by LangChain. Watch their full session for a deep dive on what's actually working (and not) for enterprises building AI agents at scale, why 2025's "year of agents" prediction didn't quite pan out, and so much more. 00:00 Introduction & welcome 01:01 MongoDB's founding principles & how they apply to AI 02:47 AI is all about unstructured data - how MongoDB fits in 03:16 Adding native search, vector search & embeddings 03:41 How different customer types use MongoDB for AI 04:43 Frontier Labs: research, inference & long-term memory 05:22 AI-native companies: the ElevenLabs case study 06:16 Enterprise customers: LangChain orchestration & internal agents 07:06 The three-legged stool: LLM + data + harness layer 08:01 The LangChain x MongoDB integration story 09:37 Open ecosystem vs. hyperscaler lock-in 10:36 Adobe's agent story: making the LLM the bottleneck 11:00 Will startup, enterprise & lab use cases converge? 11:29 Three buckets of agents: employee, partner & customer-facing 12:22 Why customer-facing agents are the real prize 13:00 A large insurance company's agent journey 14:26 The real blockers: stack choice, regulations & evals 15:44 Agent stack paralysis & why 2026 feels more stable than 2025 16:46 Token costs & the ROI of coding agents 18:06 MongoDB's internal stats: 70% of code written by AI 19:37 Token budgets & the human vs. tool cost equation 21:37 Open memory & the Context Hub announcement 22:07 System of intelligence: why context & memory will get bigger 23:37 Why real-time agents can't be built on data warehouses 24:47 Headless APIs & the future of software-via-UI 25:17 CJ's SaaS procurement strategy: cutting vendors in half 27:51 Which SaaS companies are safe vs. at risk 29:08 What makes a strong moat for Gen AI-native startups 29:56 Platform mindset: when 1 + 1 is more than 2 31:39 Wrap-up & thanks Extra resources: • Everything we shipped at Interrupt: https://www.langchain.com/blog/interrupt-2026-overview • Meet LangSmith Engine: https://www.langchain.com/blog/introducing-langsmith-engine • About LangChain: https://www.langchain.com/