Yet another generative AI venture has raised a bundle of money. And, like the others before it, it’s promising the moon.
Emergence, whose co-founders include Satya Nitta, the former head of global AI solutions at IBM’s research division, on Monday emerged from stealth with $97.2 million in funding from Learn Capital plus credit lines totaling more than $100 million. Emergence claims to be building an “agent-based” system that can perform many of the tasks typically handled by knowledge workers, in part by routing these tasks to first- and third-party generative AI models like OpenAI’s GPT-4o.
“At Emergence, we are working on multiple aspects of the evolving field of generative AI agents,” Nitta, Emergence’s CEO, told TechCrunch. “In our R&D labs, we are advancing the science of agentic systems and tackling this from a ‘first principles’ perspective. This includes critical AI tasks such as planning and reasoning as well as self-improvement in agents.”
Nitta says that the idea for Emergence came shortly after he co-founded Merlyn Mind, which builds education-oriented virtual assistants. He realized that some of the same technologies developed at Merlyn could be applied to automate workstation software and web apps.
So Nitta joined forces with fellow ex-IBMers Ravi Kokku and Sharad Sundararajan, who also co-founded Merlyn Mind, 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 language understanding, still lag in advanced planning and reasoning capabilities necessary for more complex automation tasks which are the provenance of agents,” Nitta said. “This is what Emergence specializes in.”
Emergence has a very aspirational roadmap that includes a project called Agent-E, which seeks to automate tasks like filling out forms, searching for products across online marketplaces and navigating streaming services like Netflix. An early 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, released on Monday, doesn’t perform every task itself. Rather, it functions as a kind of automatic model switcher for workflow automations. Factoring in things like the capabilities of and the cost to use a model (if it’s third party), the orchestrator considers the task to be performed — for example, writing an email — then chooses a model from a developer-curated list to complete that task.
Emergence’s orchestrator can also create workflows by “stitching” models together to perform tasks, after which it attempts to “verify” that workflows have executed successfully.
“Developers can add appropriate guardrails, use multiple models for their workflows and applications, and seamlessly switch to the latest open source or generalist model on demand without having to worry about issues such as cost, prompt migration or availability,” Nitta said.
Emergence’s orchestrator seems quite similar in concept to AI startup Martian’s model router, which takes in a prompt intended for an AI model and automatically routes it to different models depending on things like uptime and features. Another startup, Credal, provides a more basic model-routing solution driven by hard-coded rules.
Nitta doesn’t deny the similarities. But he not-so-subtly suggests that Emergence’s model-routing tech is more reliable and sophisticated than others; he also notes that it offers additional configuration features like a manual model selector, API management and a cost overview dashboard.
“Our orchestrator agent is built with a deep understanding of scalability, robustness and availability that enterprise systems need and is backed by decades of experience that our team possesses in building some of the most scaled AI deployments in the world,” he said.
Emergence intends to monetize the orchestrator with a hosted, available-through-an-API premium version in the coming weeks. But that’s only a slice of the company’s grand plan to build a platform that, among other things, processes claims and documents, manages IT systems, and integrates with customer relationship management systems like Salesforce and Zendesk to triage customer inquiries.
Toward this end, Emergence says it’s formed strategic partnerships with Samsung and touch display company Newline Interactive — both of which are existing Merlyn Mind customers, in what seems unlikely to be a coincidence — to integrate Emergence’s tech into future products.
Which 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 it’s very early days.
There’s no denying that AI agents are buzzy right now. Generative AI powerhouses OpenAI and Anthropic are developing task-performing agentic products, as are Big Tech companies, including Google and Amazon.
But it’s not obvious where Emergence’s differentiation lies, besides the sizable amount of cash out of the starting gate.
TechCrunch recently covered another AI agent startup, Orby, with a similar sales pitch: AI agents trained to work across a range of desktop software. Adept, too, was developing tech along these lines, but despite raising more than $415 million reportedly now finds itself on the brink of a bailout from either Microsoft or Meta.
Emergence is positioning itself as more R&D-heavy than most: the “OpenAI of agents,” if you will, with a research lab dedicated to investigating how agents might plan, reason and self-improve. And it’s drawing from an impressive talent pool; many of its researchers and software engineers hail from Google, Meta, Microsoft, Amazon and the Allen Institute for AI.
Nitta says that Emergence’s guiding light will be prioritizing openly available work while building paid services on top of its research, a playbook borrowed from the software-as-a-service sector. Tens of thousands of people are already using early versions of Emergence’s services, he claims.
“Our conviction is that our work becomes foundational to how multiple enterprise workflows get automated in the future,” Nitta said.
Color me skeptical, but I’m not convinced that Emergence’s 50-person team can outgun the rest of the players in the generative AI space — nor that it’ll solve the overarching technical challenges plaguing generative AI, like hallucinations and the mammoth cost of developing models. Cognition Labs’ Devin, one of the best-performing agents for building and deploying software, only manages to get around a 14% success rate on a benchmark test measuring the ability to resolve issues on GitHub. There’s clearly a lot of work to be done to reach the point where agents can juggle complex processes without oversight.
Emergence has the capital to try — for now. But it might not in the future as VCs — and businesses — express increased skepticism in generative AI tech’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 due to its focus on solving fundamental AI infrastructure problems that have a clear and immediate ROI for enterprises,” he said. “Our open-core business model, combined with premium services, ensures a steady revenue stream while fostering a growing community of developers and early adopters.”
We’ll see soon enough.
