Another AI startup has raised a bundle of money. And, like the others before it, it promises the moon.
Appearance, whose co-founders include Satya Nitta, the former head of global AI solutions at IBM’s research division, emerged from secrecy on Monday with $97.2 million in funding from Learn Capital plus lines of credit 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 those tasks to first- and third-party AI models such as OpenAI’s GPT-4o .
“At Emergence, we work on multiple aspects of the evolving field of artificial intelligence agents being created,” Nitta, CEO of Emergence, told TechCrunch. “In our R&D labs, we advance the science of agent systems and approach this topic from a ‘first principles’ perspective. This includes critical AI tasks such as planning and reasoning, as well as self-improvement in agents.”
Nitta says the idea for Emergence came shortly after she co-founded Merlyn Mind, which creates education-oriented virtual assistants. He realized that some of the same technologies developed at Merlyn could be applied to automating workstation software and web applications.
So Nitta recruited former IBMers Ravi Koku and Sharad Sundararajan to start Emergence, with the goal of “advancing the science and development of AI agents,” in Nitta’s words.
“Current AI production models, while strong in understanding language, still fall short of advanced planning and reasoning capabilities necessary for more complex agent-derived automation tasks,” Nitta said. “That’s what Emergence specializes in.”
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 in online marketplaces, and navigating streaming services like Netflix. An early form of Agent E is already available, trained on a combination of synthetic and human-annotated data. But Emergence’s first end product is what Nitta describes as an “orchestrator” agent.
This orchestrator, open source Monday, does not perform any work itself. Rather, it acts as a kind of automatic model switcher for workflow automations. Taking into account things like the capabilities and cost of using a model (if third-party), the orchestrator considers the work to be performed — e.g. compose email — and then selects a model from a developer-curated list to complete this task.
“Developers can add appropriate guardrails, use multiple models for their workflows and applications, and seamlessly transition to the latest open source or generic on-demand model without having to worry about issues such as cost, immediate migration or the availability,” Nitta said.
Emergence’s orchestrator looks quite similar in concept to the AI startup’s Martian router model, which takes a message intended for an AI model and automatically routes it to different models depending on things like runtime and capabilities. Another startup, Credal, provides a more basic model routing solution based on 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 is backed by decades of experience our team has in building some of the world’s most scalable AI deployments,” he said.
Emergence plans to monetize the orchestrator with a hosted, premium version available via API in the coming weeks. But that’s just one piece of the company’s grand plan to create 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 .
To that end, Emergence says it has formed strategic partnerships with Samsung and touchscreen company Newline Interactive – both existing Merlyn Mind customers, which seems an unlikely coincidence – to integrate Emergence’s technology into 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 did not have an answer to the second question, implying that it is too early.
There’s no doubt that AI agents are buzzing right now. Creative AI production units OpenAI and Humane they develop representative products that perform tasks, just like big tech companies like Google and Amazon.
But it’s not obvious where Emergence’s differentiation lies, other than the significant 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 on a range of desktop software. Adept has also developed technology in this direction, but despite raising more than $415 million, it is reportedly now on the brink of a bailout by Microsoft the After.
Emergence is positioning itself as more R&D-heavy than most: the “OpenAI of agents,” if you will, with a research lab dedicated to exploring how agents can plan, reason, and improve themselves. And it draws from an impressive pool of talent. Many of its researchers and software engineers come from Google, Meta, Microsoft, Amazon, and the Allen Institute for AI.
Nitta says Emergence’s driver will prioritize 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 belief is that our work is becoming fundamental to how multiple business 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 genetic AI space — nor that it will solve the main technical challenges that plague genetic AI, such as hallucinations and huge costs of model development. Devin of Cognition Labs, one of the best-performing agents for software creation and development, manages to achieve only a 14% pass rate on a benchmark test that measures the ability to solve problems on GitHub. There is clearly a lot of work to be done to get to the point where agents can manipulate complex processes unsupervised.
Emergence has the capital to try — for now. But it may not be in the future as VCs — and businesses — express increased skepticism on the way AI technology is built towards 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 of its focus on solving fundamental AI infrastructure problems that have a clear and immediate return on investment for businesses,” 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.
