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What production CX agents need after launch (Sponsored)
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Getting a CX agent live is only the first step. The harder work starts once real customers are using it.
Our new guide looks at how teams at Lyft, Vodafone, and LATAM Airlines run CX agents in production. It covers how they evaluate responses, monitor failures, and use production conversations to improve the system over time.
You’ll learn how to:
Keep prompt quality from slowing development
Build observability into the agent from the start
Catch failure modes before they affect more customers
Turn customer conversations into useful signals for support, product, and operations
Choose architectures that hold up in real CX workflows
This week’s system design refresher:
How the JVM Actually Works (Youtube video)
MCP vs RAG vs AI Agents
9 Distributed Systems Patterns You Should Know
Virtualization vs. Containerization
HTTP vs. HTTPS
How the JVM Actually Works
MCP vs RAG vs AI Agents
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MCP is an open standard protocol. It connects AI models to external tools and data sources. These can be APIs, databases, or apps like Gmail, Slack, or GitHub. So instead of you writing the integration or doing the integration for each of these applications separately, MCP basically gives you a standard way to connect to these systems.
In RAG, the model pulls the fresh information when a query comes. And this is why the model does not have to make stuff up on its own, but instead it fetches the fresh information from external data sources, like docs, PDFs, and databases, to give the most up-to-date answer to the prompt.
An AI agent is kind of an AI system where the agent performs the task autonomously and takes the decisions. And then making sure everything is working fine, instead of a chatbot, which is really a request-response.
9 Distributed Systems Patterns You Should Know
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In replication, you make exact copies of your data and store them on different servers.
In sharding, you break a large database horizontally, so different rows live on separate machines.
In consistent hashing, you distribute data across different machines or servers using a virtual circular ring.
PubSub is an asynchronous messaging pattern that decouples service creators from service consumers.
In circuit breaker distributed pattern, it stops an application from repeatedly executing an operation that is likely to fail.
In Retry with a backoff pattern, you handle temporary network glitches or brief service timeouts. It is a resiliency pattern.
In the leader election pattern, you designate a single master node to manage actions and maintain cluster state. It’s what stops two nodes from thinking they’re in charge.