Emergence aims to revolutionize AI with groundbreaking strategies to crack the AI agent code
A substantial sum of money has been raised by yet another generative AI venture. In the same vein as its predecessors, it is also making lofty promises.
On Monday, Emergence came out of stealth with $97.2 million in funding from Learn Capital and credit lines totaling over $100 million.
The company, which was co-founded by Satya Nitta, the former director of global AI solutions at IBM’s research division, emerged from stealth.
Emergence asserts that it is developing a “agent-based” system that can execute numerous tasks that are typically performed by knowledge workers.
This system accomplishes this by sending these tasks to first- and third-party generative AI models, such as OpenAI’s GPT-4o.
Nitta, Emergence’s CEO, disclosed to TechCrunch that the organization is engaged in numerous initiatives regarding the field of generative AI agents and its evolution.
“We are advancing the science of agentic systems in our R&D labs by approaching this from a ‘first principles’ perspective.” This encompasses critical AI tasks, such as self-improvement in agents and planning and reasoning.
Nitta claims that the concept for Emergence was conceived shortly after he co-founded Merlyn Mind, a company that specializes in the development of virtual assistants that are specifically designed for education.
He came to the realization that certain technologies that were developed at Merlyn could be utilized to automate web applications and workstation software.
Nitta, in his own words, recruited Ravi Kokku and Sharad Sundararajan, both of whom were former IBM employees, to establish Emergence. The organization aimed to “promote the science and development of AI agents.”
Nitta stated that current generative AI models, while effective in language comprehension, are still lacking in the sophisticated planning and reasoning capabilities required for more intricate automation tasks, which are the domain of agents. “This is the area of expertise of Emergence.”
Emergence has a highly ambitious roadmap that encompasses a project called Agent E.
This project aims to automate duties such as navigating streaming services like Netflix, searching for products across online marketplaces, and filling out forms.
A preliminary version of Agent E is currently accessible, having been trained on a combination of synthetic and human-annotated data. However, Nitta characterizes Emergence’s initial completed product as a “orchestrator” agent.
The orchestrator, open-sourced on Monday, does not execute any tasks independently. Rather, it functions as a type of automatic model switcher for workflow automations.
The orchestrator evaluates the task to be completed, such as composing an email, and then selects a model from a developer-curated list to accomplish it, taking into account factors such as the cost of using a third-party model and its capabilities.
Image Credits:Emergence
“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.
The orchestrator of Emergence appears to be conceptually similar to the model router of AI startup Martian. The model router automatically routes a prompt intended for an AI model to various models based on factors such as uptime and features.
Credal, an additional startup, offers a more fundamental model-routing solution governed by hard-coded rules.
Nitta does not dispute the similarities. However, he non-subtly implies that Emergence’s model-routing technology is more dependable than others.
He also identifies that it provides supplementary 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 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.
In the upcoming weeks, Emergence plans to monetize the orchestrator by offering a premium version that is hosted and accessible via an API.
However, this is merely a portion of the organization’s overarching strategy to establish a platform that, in addition to managing IT systems, processes claims and documents, and integrates with customer relationship management systems such as Zendesk and Salesforce to address customer inquiries.
To achieve this objective, Emergence has established strategic partnerships with Samsung and Newline Interactive, both of which are current Merlyn Mind customers. The integration of Emergence’s technology into future products is unlikely to be a coincidence.
Image Credits:Emergence
What are the specific products and when can we anticipate their release? 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.
“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.
