alexchaomander

Why did early multi-agent autonomous systems like AutoGen initially fail to meet the hype?

Multi-agent systems mapped well to our human understanding of how to organize work amongst ourselves (i.e. delegation, role responsibility, worker-to-worker communication).

But despite being amassing a ton of developer interest and producing some eye-opening demos, enterprises struggled deploying these implementations to production and opted for simpler single-agent approaches instead.

So why did they fail and what can we learn from the experience?

Watch Eric Zhu and I do a retrospective on AutoGen from our times at Microsoft and Microsoft Research in this clip from the latest episode of the Humans of AI Podcast.

https://youtu.be/2cnxea3xkzM

In this episode of Humans of AI, I sit down with my friend Eric Zhu to unpack what are the frameworks and architectures powering modern AI products today.

From our times building Semantic Kernel, Autogen, and GraphRAG at Microsoft, we offer a retrospective on the evolution of this space and where we think it'll go in 2026 and beyond.

We dive into how AI systems are actually being built today, why early agent frameworks were brittle, and what’s changing as models, tooling, and abstractions improve. Eric shares insights from hands-on experience building AI systems, including lessons around memory, orchestration, open source, and why building has become dramatically faster than even a few years ago.

We also talk about the human side of all this—how builders should think about careers in AI, avoiding burnout, and navigating a fast-moving field without getting overwhelmed.

Whether you’re an AI engineer, founder, researcher, or just curious about where things are headed, this conversation goes deep into the why behind modern AI systems.

Find the podcast on:

YouTube: https://youtu.be/9FqByZJrTN8

Spotify: https://creators.spotify.com/pod/profile/alex-chao/episodes/...

Apple Podcasts: https://podcasts.apple.com/au/podcast/the-hard-truth-about-a...

How are you building AI agents in 2026? Are you still using a framework?

s Bilt 2.0 worth it?

Like a lot of people over the past week, I asked myself that question. After an embarrassingly long time searching, reading X/Reddit and watching TikToks on the subject, I walked away more confused than informed.

What used to be a no-brainer credit card with Bilt 1.0, suddenly became a big-brained endeavor with Bilt 2.0.

Before: “Pay rent, get points.” Now: “Calculate your spend-to-rent ratio, Use Bilt Cash to offset your rent/mortgage fees (huh?), Choose multiplier tiers, etc...”

So I built myself an app.

https://is-bilt2-for-me.pages.dev

Read the full in-depth post on my newsletter Chaos Theory! https://alexchao.substack.com/p/is-bilt-20-worth-it

If the app is helpful to you or a friend, please share it!

Check out our team's blog post on how you can use automatic prompt tuning within GraphRAG to get better knowledge graphs for a specific domain!

And if you want a refresher on the details of GraphRAG, check out our walkthrough video: https://youtu.be/r09tJfON6kE?si=yP6nvp-wOoOL4fB5

What can we learn from microservices when applied to AI agents?

Introducing MicroAgents!

Check out this Semantic Kernel blogpost that my colleague Chris and I wrote exploring patterns for getting agents to use reliably use a large number of tools.

Charles Packer is a current 5th-year PhD student at UC Berkeley and author of MemGPT an open-source project teaching large language models to manage their own memory for unbounded context! Charles is also a core member of the Berkeley Artificial Intelligence Research (BAIR) Lab and Real-Time Intelligent Secure Explainable Systems (RISE) Lab where his work has spanned reinforcement learning and autonomous driving.

Check out MemGPT and join their Discord community! https://memgpt.ai/

And Charles's personal website: https://people.eecs.berkeley.edu/~cpacker/

Find the audio podcast on:

Spotify: https://open.spotify.com/episode/3vEUsgorfDwzTKRqvtco4A?si=f...

Apple Podcasts: https://podcasts.apple.com/us/podcast/charles-packer-giving-...

Humans of AI Podcast #10

Josh Fechter: Creating AI Story Writers for Books and Screenplays

A great episode with Joshua Gene Fechter the CEO of Squibler AI a startup seeking to be your AI Copywriter and Storyteller!

Josh is a growth hacking expert, a builder of communities and has written several books on software tech writing and copywriting for personal brands.

Definitely give the episode a listen!

And reach out if you want to share your story on the Humans of AI podcast!

Find the podcast on Spotify and Apple Podcasts!

Spotify: https://open.spotify.com/episode/7BcBUBnofusv23tJUsxflo?si=0...

Apple Podcasts: https://podcasts.apple.com/us/podcast/josh-fechter-creating-...

Aayush Mudgal is a Senior Machine Learning Engineer at Pinterest, currently leading the efforts around Privacy Aware Conversion Modelling. Ayush has expertise in large-scale recommendation systems, personalization, and ads marketplaces, and in the past has conducted research on intelligent tutoring systems, developing data-driven feedback to aid students in learning computer programming.

Find Aayush on LinkedIn: https://www.linkedin.com/in/aayushmudgal/

And for the books that Aayush recommends:

Why We Sleep: Unlocking the Power of Sleep and Dreams (https://amzn.to/48cVdmn)

How to Fail at Almost Everything and Still Win Big (https://amzn.to/3P5AUOG)

An Elegant Puzzle: Systems of Engineering Management (https://amzn.to/3Z91kUA)

Spotify: https://open.spotify.com/episode/3F891QndIjUIjLBySIxyuh?si=9...

Apple Podcasts: https://podcasts.apple.com/us/podcast/aayush-mudgal-building...

Humans of AI Podcast #8

A great episode with Adam Steinle, founder and CEO of hona.ai, a customizable, HIPAA-Compliant AI that searches and converts 1000s of health records into one simple patient overview!

Adam started out as a biomedical engineer, and after stints at Goldman Sachs and Meta, he returned to his roots to help medical systems communicate so that providers can learn about their patients more effectively.

Definitely give the episode a listen to hear Adam's story!

And if you want to share your story on the Humans of AI Podcast or want to partner in any way, please reach out!

Humans of AI Podcast #7

An amazing conversation with Shishir Patil the creator of the Gorilla LLM, a large language model specifically trained to use APIs!

Shishir is currently a 5th year PhD student at the University of California, Berkeley whose work broadly covers ML-Systems, LLMs, Edge-ML, and Sky computing.

Definitely give the episode a listen to hear Shishir's story.

And to read more about #GorillaLLM, check out the project page!

https://gorilla.cs.berkeley.edu

Chun Jiang is the founder of Monterey AI, an AI startup looking to be your copilot for product insights. Coming from a background of urban design and architecture, Chun has built design-centric AI products across industries like self-driving cars and geospatial data platforms.

This is a big week for the Semantic Kernel as we're making a big push for plugins! Plugins are at the heart of unlocking more potential with your Semantic Kernel applications.

They allow you to connect to external data sources and give large language models tools to be able to interact with native functions and API services.

To show our commitment to plugins, we're kicking off a weeklong hackathon open globally for people to submit their best plugins.

We'll have great prizes with more to be announced!

So, make sure to sign up for the event on Partiful and join our Discord channel to share and get help on the plugins you want to build.

Happy hacking!

Humans of AI Podcast #4

An amazing podcast session with Evan Mays, founder of Rootbeer Computer!

In this episode, we cover Evan's inspiration to get into AI from the original Iron Man films and Halo video games to his journey to winning the Scale AI hackathon with an innovative idea that GPT is all you need for backend.

We also touch on an in-depth conversation on the merits and pitfalls of current approaches to building with LLMs from vector databases and agents.

Semantic Kernel + Visual Studio Code!

Have trouble writing prompts to talk with LLMs? Do you want to have a playground to create prompts and create Semantic Kernel Skills/Plugins all in VSCode? Enter Semantic Kernel Tools.

Watch @Mark Wallace , principal software engineer, guide us through a tour of the free VSCode extension and see it in action!

In part 1, we learn how to create prompts and skills using the tool. https://youtu.be/NXspV7vo1bQ

In part 2, we learn how to create plans and execute them with the SK Tools as well as the future roadmap for the project and tips on building AI apps from Mark himself! https://youtu.be/QqnaggRV3oE

Find the extension on the VSCode Marketplace! https://marketplace.visualstudio.com/items?itemName=ms-seman...

Semantic Kernel + Visual Studio Code!

Have trouble writing prompts to talk with LLMs? Do you want to have a playground to create prompts and create Semantic Kernel Skills/Plugins all in VSCode? Enter Semantic Kernel Tools.

Watch @Mark Wallace , principal software engineer, guide us through a tour of the free VSCode extension and see it in action!

In part 1, we learn how to create prompts and skills using the tool. https://youtu.be/NXspV7vo1bQ

In part 2, we learn how to create plans and execute them with the SK Tools as well as the future roadmap for the project and tips on building AI apps from Mark himself! https://youtu.be/QqnaggRV3oE

Find the extension on the VSCode Marketplace! https://marketplace.visualstudio.com/items?itemName=ms-seman...

What do you think about this research?

Let's go around campus to hear what the people are building at CalHacks, UC Berkeley's AI and LLM hackathon!

What changes do you want to see reflected in Twitter's recommendation algorithm?

Super excited to share what my team and I have been working on the last several months: the Semantic Kernel!

We're at this cultural moment when it seems like almost anything is possible with AI. The Semantic Kernel is our way of engaging with and sharing our learnings with the open-source community expressed as an SDK.

It's still the early innings, and we might get some things wrong, but that's the beauty of building in public and in open-source!

I'm incredibly grateful to be working with such amazing teammates and now with the rest of the community!

To building the future together!

Check out the repo here: https://github.com/microsoft/semantic-kernel

What do you think of OpenAI's roadmap to achieving artificial general intelligence?

Standing out in a world of cheap plagiarism