Emergence AI Unveils Autonomous AI Agent Creation Platform, Revolutionizing Enterprise Automation

Emergence AI's new system automatically creates AI agents rapidly in realtime based on the work at hand

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Another day, another announcement about AI agents. Hailed by various market research reports as the big tech trend in 2025 -- especially in the enterprise -- it seems we can't go more than 12 hours or so without the debut of another way to make, orchestrate (link together), or otherwise optimize purpose-built AI tools and workflows designed to handle routine white collar work. Yet Emergence AI, a startup founded by former IBM Research veterans and which late last year debuted its own, cross-platform AI agent orchestration framework, is out with something novel from all the rest: an AI agent creation platform that lets the human user specify what work they are trying to accomplish via text prompts, and then turns it over to AI models to create the agents they believe are necessary to accomplish said work. This new system is literally a no code, natural language, AI-powered multi-agent builder, and it works in real time. Emergence AI describes it as a milestone in recursive intelligence, aims to simplify and accelerate complex data workflows for enterprise users. "Recursive intelligence paves the path for agents to create agents," said Satya Nitta, co-founder and CEO of Emergence AI. "Our systems allow creativity and intelligence to scale fluidly, without human bottlenecks, but always within human-defined boundaries." The platform is designed to evaluate incoming tasks, check its existing agent registry, and, if necessary, autonomously generate new agents tailored to fulfill specific enterprise needs. It can also proactively create agent variants to anticipate related tasks, broadening its problem-solving capabilities over time. According to Nitta, the orchestrator's architecture enables entirely new levels of autonomy in enterprise automation. "Our orchestrator stitches multiple agents together autonomously to create multi-agent systems without human coding. If it doesn't have an agent for a task, it will auto-generate one and even self-play to learn related tasks by creating new agents itself," he explained. A brief demo shown to VentureBeat over a video call last week appeared dully impressive, with Nitta showing how a simple text instruction to have the AI categorize email sparked a wave of new agents being created, displayed on a visual timeline showing each agent represented as a colored dot in a column designating the category of work it was designed to help carry out. Nitta also said the user could stop and intervene in this process, conveying additional text instructions, at any time. Bringing agentic coding to enterprise workflows Emergence AI's technology focuses on automating data-centric enterprise workflows such as ETL pipeline creation, data migration, transformation, and analysis. The platform's agents are equipped with agentic loops, long-term memory, and self-improvement abilities through planning, verification, and self-play. This enables the system to not only complete individual tasks but also understand and navigate surrounding task spaces for adjacent use cases. "We're in a weird time in the development of technology and our society. We now have AI joining meetings," Nitta said. "But beyond that, one of the most exciting things that's happened in AI over the last two, three years is that large language models are producing code. They're getting better, but they're probabilistic systems. The code might not always be perfect, and they don't execute, verify, or correct it." Emergence AI's platform seeks to fill that gap by integrating large language models' code-generation abilities with autonomous agent technology. "We're marrying LLMs' code generation capabilities with autonomous agent technology," Nitta added. "Agentic coding has enormous implications and will be the story of the next year and the next several years. The disruption is profound." Emergence AI highlights the platform's ability to integrate with leading AI models such as OpenAI's GPT-4o and GPT-4.5, Anthropic's Claude 3.7 Sonnet, and Meta's Llama 3.3, as well as frameworks like LangChain, Crew AI, and Microsoft Autogen. The emphasis is on interoperability -- allowing enterprises to bring their own models and third-party agents into the platform. Expanding multi-agent capabilities With the current release, the platform expands to include connector agents and data and text intelligence agents, allowing enterprises to build more complex systems without writing manual code. The orchestrator's ability to evaluate its own limitations and take action is central to Emergence's approach. "A very non-trivial thing that's happening is when a new task comes in, the orchestrator figures out if it can solve the task by checking the registry of existing agents," Nitta said. "If it can't, it creates a new agent and registers it." He added that this process is not simply reactive, but generative. "The orchestrator is not just creating agents; it's creating goals for itself. It says, 'I can't solve this task, so I will create a goal to make a new agent.' That's what's truly exciting." Bet lest you worry the orchestrator will spiral out of control and create too many needless custom agents for each new task, Emergence's research on its platform shows that it has been designed to -- and successfully carries out -- the additional requirement of winnowing down the number of agents created as it comes closer and closer to completing a task, adding agents with more general applicability to its internal registry for your enterprise, and checking back with that before creating any new ones. Prioritizing safety, verification, and human oversight To maintain oversight and ensure responsible use, Emergence AI incorporates several safety and compliance features. These include guardrails and access controls, verification rubrics to evaluate agent performance, and human-in-the-loop oversight to validate key decisions. Nitta emphasized that human oversight remains a key component of the platform. "A human in the loop is still important," he said. "You need to verify that the multi-agent system or the new agents spawned are doing the task you want and went in the right direction." The company has structured the platform with clear checkpoints and verification layers to ensure that enterprises retain control and visibility over automated processes. While pricing information has not been disclosed, Emergence AI invites enterprises to contact them directly for access and pricing details. Additionally, the company plans a further update in May 2025, which will extend the platform's capabilities to support containerized deployment in any cloud environment and allow expanded agent creation through self-play. Looking ahead: scaling enterprise automation Emergence AI is headquartered in New York, with offices in California, Spain, and India. The company's leadership and engineering team include alumni from AI research labs and technology teams at IBM Research, Google Brain, The Allen Institute for AI, Amazon, and Meta. Emergence AI describes its work as still in the early stages but believes its recursive intelligence approach could unlock new possibilities for enterprise automation and, eventually, broader AI-driven systems. "We think agentic layers will always be necessary," Nitta said. "Even as models get more powerful, generalization in the action space is incredibly hard. There's plenty of room for people like us to advance this over the next decade."

Emergence AI is using AI agents to build new AI agents in real-time - SiliconANGLE

Emergence AI is using AI agents to build new AI agents in real-time Emergence AI is emerging from the shadows with a fresh take on artificial intelligence agents, using them to create yet more agents that can perform more complicated tasks on behalf of workers. The startup, officially known as Merlyn Mind Inc., today announced the launch of a new, automated AI agent creation platform that doesn't require any coding skills. Instead, human users can simply tell it what they're trying to accomplish using natural language prompts. The platform will then fire up its AI agents, which will then set about creating more specialized AI agents to complete the designated task. Emergence AI's new agent builder was first reported by VentureBeat, which describes the system as a "no code, natural language, AI-powered multi-agent builder" that works in real time. According to the startup, its platform represents a new milestone in the area of "recursive intelligence", which refers to AI systems that can improve themselves over time without human intervention. According to Emergence AI co-founder and Chief Executive Satya Nitta, recursive intelligence is the secret sauce that makes it possible for AI agents to create new AI agents. "Our systems allow creativity and intelligence to scale fluidly without human bottlenecks, but always within human-defined boundaries," he told VentureBeat. When it's presented with an incoming task, Emergence AI will evaluate the work that needs to be performed and then check its existing registry of AI agents to see if they are capable of doing that work. If not, it will then set about autonomously creating new agents that can fulfil the requested task, and those newly built agents will get to work on the problem straight away. Taking it further, Emergence AI can also proactively create additional AI agents that will anticipate related tasks that need to be fulfilled, based on the user's previous requests. "The orchestrator figures out if it can solve the task by checking the registry of existing agents," Nitta said. "If it can't, it creates a new agent and registers it." The platform is also able to stitch together multiple AI agents and have them work with one another in a fully autonomous way, enabling a new level of enterprise automation, the company claims. With regard to the underlying large language models that power Emergence AI's agents, Nitta said customers can choose from leading LLMs including OpenAI's GPT-4o reasoning model or GPT-4.5, Anthropic PBC's Claude 3.7 Sonnet, Meta Platforms Inc.'s Llama 3.3, and many more. He added that the company has put a lot of emphasis on interoperability, so enterprises can also bring their own models and use them to power the AI agents its platform creates. The startup is targeting a wide range of use cases for its Ai agents, including data-centric tasks such as extract, transact and load pipeline creation, transformation, migration and analytics. In addition, it's also hoping to improve generative AI coding, which is an area where mistakes are still often made. According to Nitta, the idea is to marry LLM's code generating capabilities with autonomous agents that can verify the code they generate, and then suggest fixes if any problems show up. "Agentic coding has enormous implications and will be the story of the next year and the next several years," Nitta insisted. Emergence AI has implemented a number of safeguards and compliance features in place to maintain oversight of its AI agents. These include general safety guardrails to prevent agents from misbehaving, access controls and verification rubrics that assess the performance of its agents. Most importantly, though, there's always a human in the loop, Nitta said. "You need to verify that the multi-agent system or the new agents spawned are doing the task you want and went in the right direction," he pointed out. The startup has not disclosed pricing for its AI agent-powered AI agent platform, but anyone who is interested in the concept is invited to contact the company directly for details on access and costs.

Emergence AI Launches AI Agents to Create Other Agents Autonomously

This AI startup aims to minimise human intervention to build AI agents. Emergence AI, an AI startup, announced on Tuesday in a blog post that it is upgrading its platform to autonomously create and assemble AI agents. This innovation aims to minimise human intervention in enabling AI agents to build and deploy themselves, evolving through recursive self-improvement. The company stated that the heart of this system is an "orchestrator" capable of coding, planning, spawning tools and agents to handle complex tasks. The blog post explained that the orchestrator dynamically plans capabilities, reuses existing agents, and creates new ones as needed, then tests and refines these systems through simulation and self-evaluation. This allows agents to learn from failures and optimise their performance, all within human-defined goals. Furthermore, the orchestrator guides the formation of intelligent architectures without direct human coding. While the aim remains to achieve an autonomous building process, the company stated that humans remain in the loop to inspect, correct, and guide agent behaviour at any time. The company shared an example where the platform automatically generated agents to identify chips with the lowest yield, for a client in the Energy industry, demonstrating its ability to handle complex data analysis. The platform has also added an intuitive agent SDK supported by a comprehensive agent registry, facilitating seamless integration of second or third-party agents and deployment across various cloud environments. Emergence AI acknowledges potential challenges, such as bias and goal misalignment, but emphasises that enterprise settings, with structured processes and robust controls, are well-suited to mitigate these risks. The company also stated that development builds on recent advancements in AI, particularly in code generation. "We believe this shift will result in increasing both the ease-of-use of multi-agent AI and its capabilities," the company stated. Users will have to wait and see if these developments end the hype for AI agents or whether AI agents will be vibe-coded by agents.