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Crafting (create, remember, remember, fine-tuning, trust) forces developers to build and deploy intelligent agent systems using plain English commands
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First focus area: automating enterprise data pipelines, replacing vulnerable scripts and developer-intensive workflows with a flock of self-integrating agents
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Businesses can now act on raw data at speed. No tickets, manual coding, no late-night transition calls, it's a few minutes
Awakening AI, an agent frontier company, today announced the launch of Craft, the world's first natural language. A self-service platform designed to modernize one of the enterprises. It is the most costly and sustainable challenge. Craft enhances enterprise users, from analysts to executives, regardless of their technical background, transforming business goals into intelligent, self-validated multi-agent systems. Results: Understanding key metrics, increasing productivity, and fundamental changes in how companies perform data-driven work.
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Something that took weeks or months to work with a team of engineers – you can manually code ETLs, migrate schemas, clean datasets, sew tools, build ML models, and fill gaps across your data pipeline to extract actionable insights. Craft allows business users to explain their goals in plain English, deploy a swarm of specialized agents to automatically build, test and execute the required workflow. Craft's autonomous agents work across the data pipeline, reducing the need for deep technical skills and filling the key gaps caused by the global shortage of data scientists and engineers. A recent report from the US Data Science Institute highlights that data scientists' demand for 2026 will increase by 50% compared to US supply.
Crafting goes far beyond basic RPA-style automation, but we don't dismiss them. In fact, they become fundamentally simple. When automating across multiple enterprise systems to build RPA-style workflows, Craft's agents can do so easily without writing a single code by creating an agent (ACA) system. Also for teams ready to go further, Craft also serves as the launchpad for a more intelligent and adaptive agent system that can infer, self-improve and run domain-specifically. In doing so, Craft bridges the gap between task automation and truly autonomous enterprise workflows.
Working with design partners in sectors such as semiconductors, oil and gas, healthcare, telecoms and financial services, Craft is already enabling teams to dramatically reduce manual work, accelerate analysis and deliver results at business speeds.
“Craft is the launchpad for businesses to build true agent systems,” said Satya Nitta, co-founder and CEO of Emergence AI. “Use simple English to get a dynamic, self-validation agent workflow. No code, no drag and drop templates, that's it. But that's the beginning. Craft introduces advanced planning and reasoning, self-improvement, long-term memory modules.
“Crafting brings the intelligence and flexibility needed to compress weeks of work into minutes,” Nitta added. “Companies can now process machine-scale data faster, more efficiently, and at significantly lower costs. This achieves productivity gains that once required an entire team. Furthermore, this approach translates billable time into reusable software.
“Craft also marks a new category of enterprise automation: Agent Craft,” Nitta said. “This isn't just about building agents – it's full lifecycle management. A dedicated pack of agents can be replaced by intelligent systems that adapt to real-time, code, test, orchestration, and self-integration.”
Emergence has partnered with Ni/Emerson to employ hundreds of gigabytes of data analytics to improve chip yields for semiconductor companies.
“We are pleased to announce that Eran Rousseau, VP & Fellow, Ni/Emerson's Business Development and Strategic Partnerships,” said Eran Rousseau, VP & Fellow, Ni/Emerson's Business Development and Strategic Partnership. “Previously, by the time manual analysis was completed, the next production batch was ongoing, limiting timely action. Customers focus on solving key challenges rather than long-term reporting.
An intuitive interface for powerful multi-agent workflows
At the heart of Craft is a user interface that blends intuitive design with technical depth. This sets you apart from the static dashboards and rigid templates that many AI enterprise platforms offer. Users can create projects, define workflows, and generate fully functional agents using natural language. This does not require coding or canned drag and drop blocks. Craft's AI agents interpret natural language prompts, analyze existing codebases, and generate high-quality context-aware agents tailored to specific requirements.
Craft's unique approach is to create an Agent Creating Agent (ACA). This will enable the emergence of new systems earlier this year where agents dynamically build and improve other agents in real time, allowing complex multi-agent workflows with minimal human input. By enabling the creation of agent-based tools and connectors, the platform compresses design times – accelerate deployment, streamline cross-system data flow, and enable faster automation and insights across fragmented enterprise systems.
Inside the craft:
- Create – Quickly build with native or custom agents using natural language – no coding required. Integrate with tools, data, and LLM to deploy agents directly from conversations.
- Remember – Craft maintains memory throughout sessions and workflows, allowing agents to learn, adapt and maintain context over time.
- Assemble – Design workflows manually or enable agents to plan and execute dynamically. Reuse and extend automation across projects by leveraging the agent registry (a central directory of all available agents (craft-native, enterprise-created, third-party)). Includes starter kit, multi-agent support, and live/offline run views.
- Fine adjustments – Customize the behavior of your agents, edit code, and self-improve your agents through feedback and self-play. It blends developer control with no code simplicity.
- trust – With full control over privacy, scaling, and infrastructure, you can deploy flexibly across on-plame, VPC, or cloud-native environments. Enterprise grade governance using SOC 2, GDPR, audit logging, role-based access, and runtime safeguard. It incorporates full transparency, privacy and human loop surveillance.
“Enterprise constraints – regulations, structures, procedures – are no longer blockers for adopting AI. They are scaffolding for building more intelligent agents,” Nitta added. “With crafting, these constraints become the backbone of agents who learn faster, validate better, and adapt smarter.”
Target industries and use cases
Craft is especially likely to spread across multiple repositories, both legacy and modern, and the enormous volume, speed and variability of data can make analyzing in real time to support rapid operational decisions. This includes financial services, e-commerce, semiconductors, oil and gas, supply chain and logistics, SAAS and technology providers, data and research companies. In particular, craft is built for businesses that need:
- Complex workflows Includes multiple coordinated agents
- Fast Adaptation Evolving business conditions
- Cross-System Orchestration Real-time data flow
- Integrating siloed tools and processes Team and the entire platform
- Scaling workflows between functions No engineering bottleneck
The emergence also sees a bold future for craft in professional IT services and staffing. As these companies face increasing pressure to compete with AI-Native companies, craft's ambition is to evolve into a multiplier of forces to promote dramatic productivity gains and fundamentally shift the paradigm of service delivery. While that transformation is still unfolding, Craft lays the foundation for a new model of service execution with agents-equipped scalable agents. This allows companies to reinvent their products, accelerate their delivery timeline, and stay competitive in AI-driven markets.
Early influences
Craft already provides transformative results for its design partners by performing tasks such as writing and verifying ETL, migrating schemas with zero downtime, querying operational stores such as MongoDB, spin-up ML pipelines, and summarizing outputs.
- Semiconductor manufacturingIdentify millions of dollars per week through intelligent data-driven insights
- Global Telecommunications Leader: Reduced data discovery and governance efforts by 70%, promoting compliance and innovation
- 150 million people online forum: AI Agent deployment reduces unsafe images of 60 million per month, improving safety and user experience
“Engagement from all design partners sharpens the platform,” Nitta added. “Each use case adds reusable blades to the toolkit. It solidifies the craft as a dedicated foundation for domain-specific agent systems and reproducible solutions, creating sustainable moats for generic automation platforms.”
Emerging AI today announced an industry defining a partnership with Andela, one of the world's largest private markets for technical talent, to train software engineers in next-generation multi-agent system technology for implementation across the enterprise.
Carrol Chang, CEO of Andela, said: “Agent AI is transforming the way software is designed, tested and scaled, and is looking for a new kind of developer. Our partnership with Eggence will enable Andela engineers to build the skills they need to thrive in the next chapter. Ensuring the integrity and governance of the data that underlie modern enterprise applications.
Availability and pricing
Crafts are currently available in private preview as part of a gradual rollout. The platform has three layers:
- free – Perfect for individual experiments
- professional – Cost-based layer with extensions
- Companies – Custom pricing with dedicated infrastructure, governance and integration
Pro and Enterprise Tier pricing continues with actual adoption as emergence prioritizes access and learning.
“It's an enterprise AI and it's been rethinked,” Nitta added. “Craft allows enterprise leaders to interact with AI in a way that ultimately interacts with their teams – the effects of setting goals, refinement of results, and scaling. So we start by automating one of the most painful and expensive challenges: an enterprise data pipeline. A viable agent workflow.”
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