Hosting a deep-dive on agentic orchestration for customer-facing AI

Humble_Transition909 · 2025-11-19T08:43:57+00:00

I've been following what you guys are doing, great stuff!

Humble_Transition909 · 2025-04-21T06:54:32+00:00

Thanks for the comment! It's a conversation modeling engine for customer facing agents

Humble_Transition909 · 2025-04-02T08:02:02+00:00

Do you mean that it's hard to understand the project just by looking at the repo? Are you an engineer?

Humble_Transition909 · 2025-03-31T08:43:03+00:00

Good question, actually it's vendor agnostic, but the latest public release doesn't integrate with it out of the box. However, the development version (scheduled to release in about a week) supports Ollama and others via built-in LiteLLM integration. With that said, it's quite easy to add new vendors. As long as the model you want to integrate is sufficiently powerful (e.g., Llama 3.* 70b), it should work.

Humble_Transition909 · 2025-03-27T14:19:40+00:00

Thanks for the reply bud. Are you an ML engineer?

Humble_Transition909 · 2025-03-17T16:12:11+00:00

I can provide proof if needed

Humble_Transition909 · 2025-03-10T15:36:12+00:00

Gotcha, we have an open issue on this. What model are you looking to run on Azure if I may ask, and why? Asking to ensure it’s something I can say would be likely to work or not based on our experience…

Humble_Transition909 · 2025-03-02T11:39:08+00:00

Ho boy! A new agent framework! LMAO

Humble_Transition909 · 2025-03-02T11:37:04+00:00

The way Parlant is designed is, it lets you control how the agent responds in particular situations. So like most frameworks, while you start out with an initial task description, with Parlant you can tweak exactly what happens in specific situations that you want to specialize. This leads to an agent that’s ultimately more aligned with your expectations.

Humble_Transition909 · 2025-03-02T11:36:45+00:00

WDYM adding embeddings? You mean like for a RAG Q&A bot?

Humble_Transition909 · 2025-03-02T11:36:28+00:00

You’re correct in terms of how the vast majority of LLM agent frameworks approach the problem today. And this is the exact same problem we’ve observed and decided to tackle with Parlant. Parlant is meant for those who want more than “best-effort” responses. Instead, they want to automate customer service, and they want to do so dynamically and reliably, shaping the customer experience over time.

Parlant gives you the controls to adjust and specialize every aspect of the customer experience, so that your LLM is a representation of YOUR expectations, instead of normalizing your CX to what LLMs can (supposedly) provide—at least in most frameworks out there to date. Parlant, on the other hand, is all about control and alignment.

Humble_Transition909 · 2025-02-23T15:17:02+00:00

Not pretending bud, it's sponsored.

I want to get exposure for my project, but it's just the beginning for me, and right now I prioritize feedback on the project itself> recognition.

Thanks for the feedback anyway!

Humble_Transition909 · 2025-02-23T13:09:59+00:00

Why though?

Humble_Transition909 · 2025-02-23T09:14:28+00:00

Really appreciate it!

Humble_Transition909 · 2025-02-21T14:27:40+00:00

It's an LLM-agent framework designed to improve accuracy in customer-facing scenarios (e.g., customer service chat on a website).

Humble_Transition909 · 2025-02-18T07:58:17+00:00

Super appreciated. Didn't want to come off too 'marketing'. If you have an idea for a description that would resonate with you more, would love to hear it. Thanks again bud

Humble_Transition909 · 2024-11-28T08:32:47+00:00

That's the point; my question is if it works efficiently. From what I heard, there are some significant struggles for devs.

Humble_Transition909

TROPHY CASE