The idea of "agentic AI" gets thrown around a lot, and it can sound like something from a movie. Forget chatbots that just spit out answers. Imagine an AI that gets what you’re trying to do and then actually does it for you across a bunch of different apps. Basically, a digital teammate handling the grunt work while you focus on what matters.
This was the big idea behind Adept AI, one of the most talked-about and well-funded startups in the AI world. Started by some heavy hitters in AI research, Adept wanted to build the future of how we use computers. But the company’s story took a pretty dramatic turn, involving a huge pivot and a massive talent deal with Amazon. Its journey is more than just industry gossip; it teaches us a lot about how any business should think about using AI automation today.
What is Adept AI?
Adept AI’s original goal was seriously ambitious: to build a "natural language interface for everything." You could just tell it, "Find me a three-bedroom house in Austin for under $700k and list the top five options in a spreadsheet," and it would go off and perform that entire multi-step task across different websites and apps.
The team behind it was stacked, to a T. The founders were researchers from Google and OpenAI who co-authored the famous "Attention Is All You Need" paper. If that sounds familiar, it’s because it introduced the Transformer architecture, which is the engine behind modern large language models (LLMs) like GPT.
To make their vision a reality, they decided to build everything themselves, from the ground up. This meant tackling:
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Their own AI models, which they trained on a mind-boggling amount of data showing how people actually use software.
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A custom "actuation layer", which is a fancy way of saying they built software that let the AI click, type, and navigate around different applications just like a person would.
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Feedback tools so users could correct the AI when it messed up, helping it learn and get better over time.
The demos were pretty impressive. They showed agents that could check shipping availability on hundreds of e-commerce sites at once or pull key details from legal contracts and automatically update a company’s internal files. It was a bold vision for a whole new kind of AI.
This video demonstrates the initial vision of Adept AI, showing how it can learn and automate repetitive tasks for users.
The rise and pivot of Adept AI
Adept’s journey from a hyped-up, billion-dollar startup to its recent change in direction tells a fascinating story about the realities of building AI. If you’re looking to bring AI into your own company, this is some pretty important context.
The billion-dollar promise of Adept AI
With a huge goal and a rockstar team, Adept AI had investors lining up. The company raised over $415 million, pushing its valuation past the $1 billion mark. This wasn’t just a bet on another software tool; investors saw Adept as a potential leader in the race toward Artificial General Intelligence (AGI).
The hype got even bigger when giants like Microsoft and Nvidia jumped in with strategic investments, giving the startup a massive stamp of approval. For a while there, it really looked like Adept was on its way to defining the next era of AI.
The Adept AI Amazon "acqui-hire": A big change of plans
Then, in mid-2024, the story took a sharp turn. News broke that Amazon was hiring Adept’s co-founder and CEO, David Luan, along with several other founders and a big chunk of their core research and engineering team.
Just to be clear, this wasn’t a typical acquisition where Amazon bought the company. It was more of a strategic talent grab, sometimes called an "acqui-hire," packaged with a deal for Amazon to license some of Adept’s technology. In a blog post about the update, Adept was surprisingly frank about why. They explained that the astronomical cost of building and training their own models would have forced them to focus more on fundraising than on their actual product. It was a candid admission of a problem they, and many others in the AI space, were up against.
What Adept AI is today
The Adept AI that exists now is a different beast. It’s a smaller team with a new CEO, and its goals have shifted. Instead of trying to build enormous, foundational AI models from scratch, the company is now focused on creating enterprise solutions that use a mix of its existing tech and other available AI models.
This pivot brings up some big questions for any business that was keeping an eye on Adept. What does this mean for the company’s future? Is that original, ambitious vision of a do-it-all AI teammate still on their radar, or has that dream been passed on to giants like Amazon? The whole episode is a bit of a cautionary tale about the risks that come with moonshot AI projects.
Two ways to build AI agents: The Adept AI approach vs. a more practical path
Adept’s story really highlights a fundamental choice every business has when it comes to AI. Figuring out which path makes sense for you is key to picking a solution that will actually work.
The Adept AI "build it all from scratch" model
This was the original Adept AI playbook: develop your own foundational AI models from the ground up. It’s the go-big-or-go-home strategy.
To even attempt this, you need a few things: hundreds of millions of dollars, a team of the world’s best (and hardest to find) AI talent, and a whole lot of patience for years of research and development.
The biggest downside, as Adept’s pivot showed, is that this path is incredibly expensive and risky. It’s often not sustainable unless you have the bottomless pockets of a tech giant. It can take years before you have anything to show your customers, and even then, the final product might not solve the problems you have today.
The "plug-and-play" integration model
There’s a much more practical way to do things: use the powerful, best-in-class LLMs that are already out there and focus on building the application and integration layer on top. This approach is all about getting the AI to work with the tools your team already uses, so it’s useful right away.
Instead of trying to reinvent the wheel, platforms like eesel AI take this practical route. By focusing on simple, one-click integrations with help desks like Zendesk and Freshdesk, and knowledge bases like Confluence, they deliver value in minutes, not years. This lets businesses tap into the power of AI without the massive risks and costs of a long-haul R&D project.
| Feature | The "Build It All" Approach (Old Adept AI) | The "Plug-and-Play" Approach (eesel AI) |
|---|---|---|
| Setup Time | Months or years of development | Live in a few minutes |
| Required Resources | Huge funding, elite AI researchers | Totally self-serve, no developers needed |
| Core Technology | Building proprietary foundational models | Integrating with best-in-class models |
| Business Focus | AGI research and general-purpose agents | Solving specific business problems (like CX) |
| Risk Profile | High risk of pivots, unsustainable costs | Low risk, fast and predictable return |
What to look for in an AI agent platform today: Lessons from the Adept AI saga
The lessons from the Adept AI saga give us a pretty clear checklist for what businesses should actually be looking for in an AI partner.
How quickly can you see results?
The Adept story shows that long-term, complicated AI projects can easily get stuck or change course. Most businesses need solutions that provide a real return on investment quickly. You can’t afford to wait around for years.
Look for a platform that you can set up yourself and that doesn’t force you to ditch all your current software. Your team should be able to get started without having to sit through a mandatory demo or get tangled up in a long sales process. With a platform like eesel AI, you can connect your helpdesk, train the AI on your knowledge base, and start automating support tickets the same day.
Practical tools over AGI promises
While the dream of a universal AI assistant is cool, successful businesses solve real, immediate problems. Instead of getting sold on a far-off vision of AGI, look for AI agents built for specific, high-impact tasks.
Does the platform solve a problem you have right now? You want a set of specialized tools that target concrete pain points. For instance, eesel AI offers separate products like an AI Agent for full automation, an AI Copilot to help your human agents, and AI Triage for smart ticket routing. Each one is designed for a specific job within your support workflow, so you get value from day one.
The eesel AI Copilot assists human agents by drafting responses directly within their existing help desk, a practical application compared to the broader vision of Adept AI.Confident deployment with simulation and control
One of the biggest hurdles to adopting AI is trust. How can you feel comfortable letting an AI agent talk to your customers without worrying about your brand’s reputation?
This is where simulation and control become deal-breakers. You need a platform that lets you test the AI in a safe environment before it ever interacts with a real customer. You also need the power to roll it out gradually, giving you complete control over what it does.
Before you go live, you need to know exactly how the AI is going to behave. The best platforms offer a sandbox for testing. eesel AI’s simulation mode runs your AI agent on thousands of your past support tickets, giving you an accurate forecast of its resolution rate and showing you exactly how it would have responded in real situations. This lets you deploy with confidence because you know precisely what to expect.
A screenshot of the eesel AI simulation mode, which allows businesses to test their AI agent on historical data before deployment, a key lesson from the Adept AI saga.The future of Adept AI and what’s next for your business
Adept AI’s wild ride shows just how costly and risky it is to build agentic AI from scratch. Their pivot and the major deal with Amazon is a lesson for the whole industry: the future might be AGI, but the present is all about practical, real-world applications.
For most businesses, the smartest move isn’t to wait for some all-powerful AI to show up. It’s to adopt practical, integration-first solutions that solve real problems today.
Platforms like eesel AI offer a faster, safer, and more accessible way to bring AI agents into your work. Instead of multi-year roadmaps and nine-figure funding rounds, you can get results in minutes.
Ready to see what a practical AI agent can do for your support team? Start automating in minutes with eesel AI.
Frequently asked questions
Adept AI’s initial vision was to build a "natural language interface for everything," allowing users to tell an AI what they wanted to do across different apps, and the AI would perform the multi-step task automatically. This aimed to create a digital teammate that could handle complex workflows.
Adept AI pivoted primarily due to the astronomical costs and resources required to build and train their own foundational AI models from scratch. They realized this path would force them to focus more on fundraising than product development, leading to a shift in focus.
The current Adept AI, with a smaller team and new leadership, is now focused on creating enterprise solutions. Instead of building foundational models, they aim to integrate a mix of their existing technology with other available AI models to serve specific business needs.
It was not a typical acquisition where Amazon bought the entire company. Instead, it was an "acqui-hire" deal where Amazon hired Adept’s co-founder and CEO, David Luan, along with a significant portion of their research and engineering team, combined with a technology licensing agreement.
The original Adept AI aimed to build all foundational AI models and actuation layers from scratch, requiring massive investment and long development times. In contrast, the "plug-and-play" model utilizes existing powerful LLMs and focuses on building quick integrations and application layers for immediate business value.
Businesses should learn that building AI from scratch is incredibly costly and risky, often leading to pivots. It highlights the importance of seeking AI solutions that provide quick returns, solve practical problems today, and offer confident deployment with control, rather than chasing distant AGI promises.
