Emergence thinks it can crack the AI agent code
Yet another generative AI venture has raised a bundle of money. And, like the others before it, itās promising the moon.
Emergence, whose co-founders include Satya Nitta, the former head of global AI solutions at IBMās research division, today 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 āagent-basedā 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 OpenAIās GPT-4o.
āAt Emergence, we are working on multiple aspects of the evolving field of generative AI agents,ā Nitta, Emergenceās CEO, told TechCrunch in an interview. āIn our R&D labs, we are advancing the science of agentic systems and tackling this from a āfirst principlesā perspective.Ā This includes critical AI tasks such as planning and reasoning as well as self-improvement in agents.ā
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 recruited fellow ex-IBMers Ravi Koku and Sharad Sundararajan to launch Emergence, with the goal of āadvancing the science and development of AI agents,ā in Nittaās words.
āCurrent 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,ā Nitta said. āThis is what Emergence specializes in.ā
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 Emergenceās first finished product is what Nitta describes as an āorchestratorā agent.
This orchestrator, open-sourced today, doesnāt perform any tasks 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 itās third-party), the orchestrator considers the task to be performed ā e.g. writing an email ā then chooses a model from a developer-curated list to complete that task.
āDevelopers 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,ā Nitta said.
Emergenceās orchestrator seems quite similar in concept to AI startup Martianās model router, which takes in a prompt intended for an AI model and automatically routes it to different models depending on criterion like uptime and features. Another startup, Credal, provides a more basic model-routing solution driven by hard-coded rules.
Nitta doesnāt deny the similarities. But he not-so-subtly suggests that Emergenceās model-routing tech is more reliable than others ā and notes that it offers additional configuration features like a manual model selector, API management and a cost overview dashboard.
āOur 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,ā he said.
Emergence intends to monetize the orchestrator with a hosted, available-through-an-API premium version in the coming weeks. But thatās only a slice of the companyās 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 itās formed strategic partnerships with Samsung and touch display company Newline Interactive ā both of whom are existing Merlyn Mind customers, in what seems unlikely to be a coincidence ā to integrate Emergenceās tech into future products.
Which specific products and when can we expect to see them? Samsungās WAD interactive displays and Newlineās Q and Q Pro series displays, Nitta said, but he didnāt have an answer to the second question ā implying that itās very early days.
Thereās 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 itās not obvious where Emergenceās differentiation lies, besides the sizeable 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 ā the āOpenAI of agents,ā if you will, with a research lab dedicated to investigating how agents might plan, reason and self-improve. And itās 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 Emergenceās 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 Emergenceās services, he claims.
āOur conviction is that our work becomes foundational to how multiple enterprise workflows get automated in the future,ā Nitta said.
Color me skeptical, but Iām not convinced that Emergenceās 50-person team can outgun the rest of the players in the generative AI space ā nor that itāll solve the overarching technical challenges plaguing generative AI, like hallucinations and the mammoth cost of developing models. Cognition Labsā 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. Thereās 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 ā for now. But it might not in the future as VCs ā and businesses ā express increased skepticism in generative AI techās path to ROI.
Nitta, projecting the confidence of someone whose startup just raised $100 million, asserted that Emergence is well-positioned for success.
āEmergence is resilient due to its focus on solving fundamental AI infrastructure problems that have a clear and immediate ROI for enterprises,ā he said. āOur open-core business model, combined with premium services, ensures a steady revenue stream while fostering a growing community of developers and early adopters.ā
Weāll see soon enough.