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Building Frontier CX Agents | Interrupt 26

LangChain16 June 2026Watch on YouTube

Part of series

Ep. 6 · 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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Description

In this keynote from Interrupt 2026, Cisco Customer Experience Fellow and Chief Architect Carlos Pereira pulls back the curtain on how one of the world's largest enterprise organizations is building and scaling frontier AI agents for its Customer Experience (CX) division. Carlos walks through Cisco's real-world journey from early chatbots and the "Agentic Foundation" to a fully AI-native "Renews Teammate" running in production across a $26B+ recurring revenue business. He shares candid lessons learned: why 95% accuracy wasn't enough for adoption, how bolting AI onto broken workflows can actually accelerate failure, and why the key to scaling agentic systems is keeping supervisors lean, introducing domain-specific planners, and building in self-correction by default. You'll come away with a practical architecture playbook—covering supervisor graphs, nested subgraph planners, deterministic task workflows, routing-first design, and long-term memory—plus an honest look at the organizational challenges of getting people to actually use these systems. Building Frontier CX Agents | Interrupt 26 00:00 Introduction & Cisco CX Overview 01:22 The Land, Adopt, Expand & Renew Model 02:36 From ChatGPT Hype to B2B Agentic AI 03:16 Why 2026 Is the Year of AI-Native Business Workflows 04:18 The Problem with Bolting AI onto Broken Workflows 05:01 The Agentic Foundation (Built in 2025) 05:49 Evolving to the "Renews Teammate" Concept 06:29 Architecture Deep Dive: Supervisor, Planner & Agent Layers 07:51 Introduction of the Planner Node 10:21 Zooming In: How the Supervisor Handles Complex Queries 12:14 Dynamic Replanning & Self-Correction Loops 14:43 Deterministic Task Workflows vs. LLM Reasoning 16:57 The Adoption Plateau: Why Users Ghosted the System 18:11 Forced Curiosity & Evolving from Chatbot to Teammate 18:50 Flipping the Mindset: Humans Help Software, Not Vice Versa 20:29 Key Features: Long-Term Memory, Scheduling & Proactive Intelligence 20:53 Lessons Learned: System Level 21:56 Lessons Learned: Agentic Infrastructure 23:15 Closing Remarks 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/

What you'll learn

  • AI agents only succeed when underlying workflows are fixed first; bolting AI onto broken processes accelerates failure
  • Effective agentic architecture has three layers: supervisor (query routing), planner (step-by-step planning), and agents (task execution)
  • High accuracy alone (95%) is insufficient for adoption; users must see AI genuinely transform and simplify their work

Frequently asked questions

Why did users initially resist the 'Renews Teammate' system despite 95% accuracy?
The systems were built on broken workflows and didn't deliver enough real value in daily work. Users need to see AI genuinely simplify their work, not just achieve high accuracy scores.
What are the three layers that form the foundation of Cisco's agentic architecture?
The supervisor layer handles query routing, the planner layer manages step-by-step planning, and the agent layer executes tasks. These separate layers make the system more predictable and scalable.
How does Cisco build self-correction into its AI agents?
By incorporating dynamic replanning and self-correction loops as default, agents can adjust their approach when a task doesn't succeed immediately.
What is the difference between deterministic task workflows and LLM reasoning?
Deterministic workflows follow predetermined steps and are more reliable for standard tasks, while LLM reasoning is more flexible for complex, unexpected situations.

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