[Ep 029] From UI to LLM Building a Custom AI Agent End to End

Описание: Custom agentic AI doesn’t have to be confusing. In this episode, we break down agentic AI architecture from the user interface all the way to tools, memory, LLM selection, and monitoring—so you can design a custom agent that actually works in production.

Watch next: Agentic AI playlist for more episodes:    • Agentic AI  

Key takeaways
➡️ The end-to-end blueprint: how users interact, where agent logic runs, how tools connect, and how context is retrieved
➡️ The 6 core build decisions behind most custom agent stacks (UI, hosting, tools/MCP, memory/RAG, LLMs, monitoring)
➡️ A practical example: a Zendesk ticket triage agent that can act in Salesforce, use docs via RAG, store memory, and report results back

Chapters
00:00 Why architecture matters
00:28 Why go custom vs platforms
01:57 Start with the interaction layer (UI / Slack / APIs)
04:32 Hosting the agent logic in cloud (AWS/GCP/Azure)
05:52 Tools + integrations (ETL, endpoints, services)
07:37 LLM choice: self-hosted vs API + multi-LLM routing
09:18 Monitoring and observability (usage, cost, health)
10:21 The six core decisions overview
11:16 Example architecture: Zendesk + Salesforce agent stack
15:56 Flexibility vs complexity + multiple front ends
18:58 Wrap

Links & resources
Companion architecture artifact: https://drive.google.com/file/d/1O3D0...
Gavin Franklin on LinkedIn   / gavin-franklin-067247170  
Tim Harting on LinkedIn:   / timothy-harting  
Cirrius Solutions https://cirriussolutions.com/contact/
Cirrius Blog: https://cirriussolutions.com/blog/

Apple Podcasts: https://podcasts.apple.com/ca/podcast...
Spotify: https://open.spotify.com/show/1of2KEM...
Youtube:    • Cirrius Talk  
Greg Banks on LinkedIn:   / greg-banks-9785197  
Jason Fowler Music: https://www.jasonfowlermusic.com/

Questions / follow-up: [email protected]

Music credit: Thanks to Jason Fowler for allowing us to use “Mystery Road”

Guest info (from this episode)
Tim Harting and Gavin Franklin discuss practical architecture decisions required to build custom AI agents across enterprise systems, including hosting, memory, RAG, MCP tools, and monitoring.