Another generative AI company has raised a lot of money. And like the others before him, he promises the moon.
Emergence, whose co-founders include Satya Nitta, former head of global AI solutions at IBM’s research division, emerged from stealth on Monday with $97.2 million in funding from Learn Capital as well as credit lines totaling more than $100 million. Emergence claims to build an “agent-based” system capable of performing many tasks typically handled by knowledge workers, in part by routing these tasks to first-party and third-party generative AI models like GPT-4o from OpenAI.
“At Emergence, we are working on multiple aspects of the evolving field of generative AI agents,” Emergence CEO Nitta told TechCrunch. “In our R&D labs, we are advancing the science of agentic systems and approaching this problem from a “first principles” perspective. This includes critical AI tasks such as planning and reasoning as well as agent self-improvement.
Nitta says the idea for Emergence came shortly after he co-founded Merlyn Mind, which creates virtual assistants focused on education. He realized that some of the technologies developed at Merlyn could be applied to automate desktop software and web applications.
So Nitta recruited fellow ex-IBMers Ravi Kokku and Sharad Sundararajan to launch Emergence, with the goal of “advancing the science and development of AI agents,” in Nitta’s words.
“Current generative AI models, while powerful in terms of language understanding, still lag behind in terms of the advanced planning and reasoning capabilities needed for the more complex automation tasks that agents come from,” Nitta said. “That’s Emergence’s specialty.”
Emergence has a very ambitious roadmap that includes a project called Agent E, which seeks to automate tasks like filling out forms, searching for products on online marketplaces, and navigating streaming services like Netflix. A first form of Agent E is already available, trained on a mix of synthetic and human-annotated data. But Emergence’s first finished product is what Nitta describes as an “orchestrator” agent.
This orchestrator, open source on Monday, does not perform any tasks itself. Rather, it functions as a sort of automatic template picker for workflow automation. Taking into account things like the capabilities and cost of using a model (if it’s a third party), the orchestrator considers the task at hand (e.g. writing an email ) then chooses a pattern from a developer-curated list to accomplish it. stain.
“Developers can add appropriate guardrails, use multiple models for their workflows and applications, and seamlessly upgrade to the latest open source or general-purpose model on-demand without having to worry about issues like cost, rapid migration or availability,” Nitta said.
Emergence’s orchestrator seems quite similar in concept to AI startup Martian’s model router, which takes a prompt intended for an AI model and automatically routes it to different models based on things like availability and features. Another startup, Credal, offers a more basic model routing solution, driven by hard-coded rules.
Nitta doesn’t deny the similarities. But it not-so-subtly suggests that Emergence’s model routing technology is more reliable than others; he also notes that it offers additional configuration features such as a manual model selector, API management, and a cost overview dashboard.
“Our orchestrator agent is built with a deep understanding of the scalability, robustness and availability that enterprise systems need and draws on decades of experience our team has in creating some of the largest-scale AI deployments in the world,” he said.
Emergence intends to monetize the orchestrator with a hosted premium version available via an API in the coming weeks. But that’s only part of the company’s larger plan to create a platform that, among other things, processes complaints and documents, manages IT systems and integrates with relationship management systems client like Salesforce and Zendesk to sort customer requests.
To that end, Emergence says it has formed strategic partnerships with Samsung and touchscreen company Newline Interactive – both of which are existing Merlyn Mind customers, which does not appear to be a coincidence – to integrate Emergence’s technology in future products.
What specific products and when can we expect to see them? Samsung’s WAD interactive displays and Newline’s Q and Q Pro series displays, Nitta said, but he didn’t have an answer to the second question, implying that this is just the beginning.
There’s no denying that AI agents are very busy these days. Generative AI powerhouses OpenAI and Anthropic are developing powerful agent products, as are major tech companies including Google and Amazon.
But it’s not clear where Emergence’s differentiation lies, other than the sheer amount of cash upfront.
TechCrunch recently covered another AI agent startup, Orby, with a similar selling point: AI agents trained to work on a range of desktop software. Adept was also developing technology along these lines, but despite raising more than $415 million, it now reportedly finds itself on the verge of being bailed out by Microsoft or Meta.
Emergence positions itself as more R&D-focused than most: “the OpenAI of agents,” if you will, with a research lab dedicated to studying how agents can plan, reason, and communicate. self-improvement. And he draws from an impressive talent pool; many of its researchers and software engineers come from Google, Meta, Microsoft, Amazon and the Allen Institute for AI.
Nitta says Emergence’s common thread will be to prioritize freely available work while building paid services on top of its research, a playbook borrowed from the software-as-a-service industry. Tens of thousands of people are already using the first versions of Emergence services, he says.
“Our belief is that our work becomes fundamental to how many companies’ workflows will be automated in the future,” Nitta said.
Be skeptical, but I’m not convinced that Emergence’s 50-person team can outperform the rest of the players in the generative AI space – nor that it will solve the overarching technical challenges plaguing generative AI , like hallucinations and the colossal cost of generative AI. develop models. Devin of Cognition Labs, one of the most successful agents in software creation and deployment, manages to beat only a 14% pass rate on a benchmark test measuring problem-solving ability on GitHub . There is clearly a lot of work to be done to get to the point where agents can juggle complex processes without oversight.
Emergence has the capital to try – for now. But that may not be the case in the future as venture capitalists – and businesses – express increased skepticism about generative AI technology’s path to ROI.
Nitta, projecting the confidence of someone whose startup just raised $100 million, asserted that Emergence is well-positioned for success.
“Emergence is resilient because it focuses on solving fundamental AI infrastructure problems that have a clear and immediate ROI for businesses,” he said. “Our open-core business model, combined with premium services, ensures a stable revenue stream while fostering a growing community of developers and early adopters. »
We’ll see soon enough.