Artificial intelligence is moving from the lab bench to the production floor, and the companies enabling that transition are building the infrastructure that defines how agents are created, trained, deployed, and governed. The companies doing it most consequentially are not wrapping chat interfaces around existing models; they are constructing the runtimes, the training environments, the reasoning layers, and the enterprise operating systems that turn AI from a demo into a workforce. From open-source TypeScript frameworks to frontier AI labs for national security, from knowledge automation platforms to simulation environments where agents learn by doing, these are the scale-ups building the technical foundation of the agentic economy.
Companies are listed in alphabetical order. This list is non-exhaustive.
1. AUCTOR
Headquarters: New York, NY | Total Funding: ~$20M
Enterprise software implementations fail at an astonishing rate, not because the software is wrong but because the process of implementing it is broken. Discovery sessions scatter decisions across emails, meeting notes, and slide decks. Scoping documents go stale before the project closes. Tribal knowledge walks out the door when a project lead moves on, forcing every subsequent engagement to restart from nothing. The result is that even the best enterprise software consistently under-delivers on its promise, because the implementation layer has never been systematized.
Auctor is the AI-native system of action for the full enterprise software implementation lifecycle. Founded by William Sun, the platform functions as an agentic operating system for implementation teams and system integrators: automatically ingesting context from discovery sessions, structuring requirements, generating scopes, plans, and documentation, and maintaining traceability across every phase of delivery. The company raised a $20 million Series A led by Sequoia Capital in April 2026, with participation from M12 (Microsoft’s Venture Fund), HubSpot Ventures, Workday Ventures, OneStream, and Y Combinator. Customers including Atlassian’s largest global partner Valiantys, which serves 65 Fortune 500 companies, are reporting 80% efficiency gains across discovery and design phases. Sequoia partner Julien Bek described the problem as “as universal as it is underserved.” For implementation teams that have spent years managing projects through a patchwork of spreadsheets and institutional memory, Auctor is the system that finally makes their best work repeatable.
2. DAYTONA
Headquarters: New York, NY | Total Funding: ~$24M
Cloud infrastructure was built for stateless, immutable production workloads managed by human engineers. It was not designed for AI agents, which need to launch sandboxed environments in milliseconds, explore multiple decision paths in parallel, snapshot mid-execution to preserve state across failures, and run arbitrary code without exposing production systems to risk. The gap between what today’s cloud provides and what AI agents actually need is one of the biggest underappreciated constraints in the agentic economy.
Daytona is closing that gap by building what it calls a new primitive: the sandbox. These are programmatic, composable computers where CPU, memory, storage, GPU, networking, and the operating system can be configured on demand, then started, paused, or snapshotted at any point. The company raised $24 million in a Series A led by FirstMark Capital in February 2026, with participation from Pace Capital, Upfront Ventures, Datadog, and Figma Ventures. Founded by Ivan Burazin and Vedran Jukic, Daytona hit a $1 million revenue run rate in under three months and doubled it six weeks later. Customers range from early-stage Y Combinator companies to Fortune 100 enterprises. FirstMark partner Matt Turck, who joined the board, framed the opportunity as building “a computer for every agent,” with instant startup, persistent state, and the tooling agents need to write code, use Git, and execute safely at scale.
3. DECCAN AI
Headquarters: San Francisco, CA | Total Funding: ~$25M+
Frontier AI models are only as capable as the data and environments used to train and evaluate them. As the industry moves past web-scale pre-training into post-training, RLHF, and agentic evaluation, the quality of training data, reward signals, and evaluation pipelines has become the primary differentiator between models that work in production and models that fail in it. Most enterprise AI deployments are not failing because the underlying models are weak; they are failing because the post-training stack is inadequate.
Deccan AI is the post-training and production infrastructure company supplying the data, environments, and evaluation tools that models need to handle high-stakes business logic. Founded in 2023 by Rukesh Reddy, its product suite spans STARK RL environments for reinforcement learning, Helix for hybrid human-AI evaluation, and EnterpriseOS for scaled operations automation. Deccan grew 10x in the year preceding its funding, working with a majority of the Magnificent 7 and clients including Google and Snowflake, supported by a global network of over one million domain experts. The company raised $25 million in March 2026 led by A91 Partners, with participation from Susquehanna and existing investor Prosus Ventures. For the AI labs and enterprises trying to move from prototype to production, Deccan is the infrastructure that makes the crossing reliable.
4. DEEPSEE.AI
Headquarters: Draper, UT | Total Funding: ~$21.8M
Enterprise organizations generate enormous volumes of operational knowledge: process documentation, institutional expertise, and workflow logic built up over years of execution. The problem is that this knowledge exists in unstructured, siloed, and undiscoverable forms across documents, email chains, and the memories of employees who learned how the business actually works rather than how the documentation says it should. When those employees leave, or when a new system needs to be deployed, the knowledge has to be reconstructed from scratch.
DeepSee.ai is the Knowledge Process Automation platform that changes that. Its cloud-native system uses AI-powered agents to automate complex, knowledge-intensive banking and financial services workflows, connecting front, middle, and back-office operations through pre-trained agents with deep domain understanding. The company has raised $21.8 million across multiple rounds from investors including Broadridge Financial Solutions, BankTech Ventures, the Independent Community Bankers of America, and EJF Capital, building a customer base across community banks, capital markets participants, and enterprise financial institutions. By transforming raw operational data into structured, actionable knowledge, DeepSee.ai gives AI agents the context they need to execute rather than merely respond.
5. DEEPTUNE
Headquarters: New York, NY | Total Funding: ~$43M
Frontier language models have been trained on essentially everything humanity has written. The training data is running out, and the ceiling on capability improvement from pre-training alone is approaching. The next wave of AI progress will come not from more text but from environments: high-fidelity simulations where agents learn by doing, accumulating experience through trial and error in settings that mirror the professional digital workspaces where they will eventually be deployed.
Deeptune is building those environments. Its training gyms are high-fidelity virtual replicas of professional digital workspaces, including tools used by software engineers, customer support teams, accountants, and DevOps engineers. Inside those simulations, AI agents learn through reinforcement learning, guided by reward systems that push them toward better task completion across multi-step workflows in tools like Slack, Salesforce, and finance and monitoring platforms. The company raised $43 million in a Series A led by Andreessen Horowitz in March 2026, with participation from 776, Abstract Ventures, and Inspired Capital, along with angels including OpenAI researcher Noam Brown. Deeptune has already built hundreds of training gyms for some of the world’s leading AI labs. CEO Tim Lupo’s framing is precise: “You wouldn’t have a pilot who has only ever read books fly a plane. What we build are essentially the flight simulators for AI doing work across the economy.”
6. DIFY.AI
Headquarters: Sunnyvale, CA | Total Funding: ~$30M | Valuation: ~$180M
The gap between an AI prototype and a production-grade AI application is wider than most organizations anticipate. Connecting a language model to a business workflow requires prompt management, tool integration, knowledge retrieval, observability, governance, and the infrastructure to manage all of it across updates, errors, and scaling. Most teams either rebuild this stack from scratch for every use case or accept that their AI systems will remain permanently in demo mode.
Dify.AI is the open-source platform that closes that gap. With a visual workflow builder, RAG pipeline infrastructure, multi-model routing, observability tooling, and a plugin marketplace with over 100 integrations, Dify gives any team the infrastructure to move from prototype to production without rebuilding from zero each time. The platform runs on more than 1.4 million machines across 175 countries, with commercial versions used by over 2,000 teams and 280 enterprises including Maersk, ETS, Anker Innovations, and Novartis. Dify raised $30 million in a Series Pre-A led by HSG in March 2026, with participation from GL Ventures, Alt-Alpha Capital, and 5Y Capital, at a valuation of approximately $180 million. The platform ranks among GitHub’s most-starred open-source repositories of all time, with over 144,000 stars. For teams that want to own their AI infrastructure rather than rent it, Dify is where production begins.
7. EDRA
Headquarters: New York, NY | Total Funding: ~$30M
Every enterprise runs on operational knowledge that has never been written down. It lives in support tickets, email threads, chat logs, and the habits of experienced employees who learned how the business actually works rather than how the documentation says it should. When AI systems are deployed into these environments, they typically start blind, trained on generic data with no understanding of how this specific organization operates. The result is agents that look impressive in demos and fail in production.
Edra was founded by Eugen Alpeza and Yannis Karamanlakis, both former Palantir engineers, to solve that problem. The platform connects to a company’s existing systems within minutes, ingests support tickets, emails, logs, and communications without manual configuration, and reverse-engineers how work is actually performed into a Living Playbook: a structured, white-box library of executable knowledge that gives AI agents the context to act. The playbook continuously learns from actual employee behavior and evolves as the business changes. The company raised $30 million in a Series A led by Sequoia Capital in March 2026, with participation from 8VC and A*. Already in production at HubSpot, ASOS, and Cushman & Wakefield, Edra analyzed 150,000 support conversations at HubSpot, surfaced over 600 knowledge base updates, and cut human handoffs by 12%. For enterprises where the gap between documented process and actual process is where AI deployments die, Edra is the company that closes it.
8. GUILD.AI
Headquarters: Austin, TX | Total Funding: Undisclosed
Machine learning development has a reproducibility and collaboration problem. Experiments run in notebooks or local scripts generate results that cannot be reliably reproduced, compared, or shared across teams. Hyperparameter tuning is manual and time-consuming. The relationship between a model’s training configuration and its deployed performance is frequently opaque. The result is that even organizations with sophisticated ML teams spend disproportionate time on operational plumbing rather than model work itself.
Guild.ai is the developer platform that brings operational discipline to machine learning workflows. Its open-source framework enables ML engineers to run, track, compare, and reproduce experiments with minimal changes to existing code, integrating with any training framework, any cloud provider, and any model architecture without forcing migration to a proprietary ecosystem. The platform’s design philosophy is tool-agnostic and framework-independent, meaning teams can adopt it incrementally without rearchitecting their ML stack. Guild provides automated hyperparameter search, experiment tracking, artifact management, and pipeline orchestration for ML teams that want reproducible, auditable development workflows. For ML practitioners who have spent years recreating experiments from memory because the original run was never properly captured, Guild.ai is the operational infrastructure that should have been there from the beginning.
9. LYZR
Headquarters: Jersey City, NJ | Total Funding: ~$22.5M | Valuation: ~$250M
Enterprise AI adoption is stuck in a specific kind of limbo. Organizations want to deploy AI agents that automate real workflows, but they cannot send sensitive customer, financial, or operational data to external cloud providers, and they lack the infrastructure to build and govern a reliable AI workforce internally. Approximately 60% of enterprises remain in the experimentation phase, running pilots that never reach production because the deployment layer has never been properly built.
Lyzr provides what it calls the “Third Way” for enterprise AI: full-stack agent infrastructure that combines the flexibility of open-source with the security of a managed platform, running entirely within the customer’s own cloud environment or on-premise servers. Its Agent Studio enables both professional developers and no-code business users to build, govern, and deploy secure, model-agnostic AI agents, with over 100 production-ready agents across banking, insurance, HR, marketing, and sales. Founded in 2023 by CEO Siva Surendira, the company raised $14.5 million in a Series A+ led by Accenture in March 2026, with participation from Rocketship VC, quintupling its valuation to $250 million in under six months. Clients include AWS, Hitachi, NTT Data, and Nvidia, with revenue growing more than 300% in each of the two quarters preceding the raise. For regulated enterprises that need AI to stay inside their walls, Lyzr is the infrastructure that makes that possible.
10. MASTRA
Headquarters: San Francisco, CA | Total Funding: ~$13M
The JavaScript and TypeScript developer community represents hundreds of millions of lines of production code and millions of engineers who have never had a first-class agent framework in their native ecosystem. Every major AI agent framework built to date has been designed for Python. That is not a minor inconvenience; it is a structural barrier that prevents the majority of web and full-stack developers from building agents in the language they know without switching ecosystems or maintaining a parallel codebase.
Mastra is the TypeScript-first agent framework built by the team behind Gatsby, the React static site generator. It provides a comprehensive suite of AI primitives including graph-based workflow orchestration, multi-model routing across 90 or more providers, memory systems, RAG, evaluation tools, and human-in-the-loop support, designed for TypeScript from the ground up rather than ported from Python. The company raised $13 million in seed funding from Y Combinator, Paul Graham, Gradient Ventures, and over 120 investors. It hit version 1.0 in January 2026 after reaching 150,000 weekly downloads in its first year, making it one of the fastest-growing JavaScript frameworks ever measured. PayPal, Adobe, Elastic, Docker, and Marsh McLennan (which deployed a Mastra-based agentic search tool to 75,000 employees) are among companies in production with the framework. Replit’s Agent 3 uses it to build agents at scale. For the JavaScript and TypeScript community, Mastra is the framework they did not have to build themselves.
11. NEOCOGNITION
Headquarters: San Francisco, CA | Total Funding: ~$40M
Today’s AI agents are generalists. They succeed at routine tasks in known environments and fail unpredictably at anything requiring adaptation to novel situations, learning from mistakes across sessions, or developing the accumulated expertise that distinguishes a specialist from a capable beginner. The underlying problem is architectural: current agents do not learn on the job. They execute against static capabilities, and when they fail, that failure generates no lasting improvement. The vision of agents that become more capable the longer they work for an organization remains largely unrealized.
NeoCognition is the research lab building agents that actually learn on the job and become specialized experts through deployment rather than pre-training alone. Founded by the research team behind one of the most established AI agent labs in the country, the company emerged from stealth in April 2026 with $40 million in seed funding co-led by Cambium Capital and Walden Catalyst Ventures, with participation from Vista Equity Partners and notable angels including Intel CEO Lip-Bu Tan and Databricks co-founder Ion Stoica. CEO Kevin Su has noted that current agents from leading providers complete tasks as intended only about 50% of the time. NeoCognition’s mission is to close that gap through agents that improve with every deployment, accumulating specialization the same way a new employee becomes an expert through months of doing the actual work.
12. POETIQ
Headquarters: San Francisco, CA | Total Funding: ~$45.8M
Large language models are impressive databases. They encode vast amounts of human knowledge and retrieve it with remarkable fluency. What they are not, structurally, is reasoning engines. The current approach to improving their problem-solving capabilities through reinforcement learning is real but slow: weeks of training time, enormous compute costs, and access restrictions that put it out of reach for all but the largest AI companies. An MIT study of 300 public AI implementations published in August 2025 found that 95% of organizations investing in generative AI were getting zero return, largely because the use cases requiring genuine reasoning remain out of reach.
Poetiq is the AI meta-system that changes the economics of reasoning. Founded by co-CEOs Shumeet Baluja and Ian Fischer, both former Google DeepMind researchers with over two decades building AI systems at Google, the platform sits on top of any frontier model including GPT, Claude, Gemini, and Llama, and uses recursive self-improvement to generate specialized agents for hard problems in hours rather than weeks. The company raised $45.8 million in seed funding co-led by FYRFLY Venture Partners and Surface Ventures in January 2026, with participation from Y Combinator, 468 Capital, Operator Collective, Hico Ventures, and Neuron Venture Partners. Poetiq achieved a new state-of-the-art on the ARC-AGI 2 benchmark at roughly half the cost per task of the prior leader. When OpenAI released GPT-5.2, Poetiq immediately incorporated it and jumped to 75% accuracy on the benchmark, a 16-point improvement over the previous leader. OpenAI co-founder and president Greg Brockman took public notice.
13. SMACK TECHNOLOGIES
Headquarters: El Segundo, CA | Total Funding: ~$32M
Military decision-making operates at the intersection of extreme time pressure, incomplete information, and catastrophic consequence. Traditional decision loops in contested environments can take up to 96 hours, constrained by siloed sensors, fragmented data systems, and planning processes designed for a slower world. Peer adversaries now demand decisions in seconds across air, sea, land, cyber, and space domains simultaneously. The static models and general-purpose AI tools that dominate commercial AI are structurally unsuited to this environment.
Smack Technologies is the first frontier AI lab purpose-built for national security, co-founded by MARSOC veterans Andy Markoff and Clint Alanis, with over two decades of Marine special operations experience between them. Its dual product suites, Omega and Alpha, are built on domain-specific AI models powered by deep reinforcement learning, trained in synthetic warfare environments by warfighters who understand the actual structure of high-stakes military decision-making. Omega converts commander intent into executable campaign plans in minutes, breaking down decision-making silos across all time horizons. The company raised $32 million in combined seed and Series A funding led by Geodesic Capital and Costanoa Ventures in March 2026, and already holds seven-figure contracts from the Joint Fires Network and Marine Corps Warfighting Lab. CTO Dan Gould, formerly VP of Technology at Tinder, leads engineering alongside Yale PhD head of decision sciences Eli Levin. The DoD allocated a record $13.4 billion for AI and autonomy in FY2026. Smack is building the reasoning layer that makes that investment matter in the field.
14. STANDARD TEMPLATE LABS
Headquarters: New York, NY | Total Funding: ~$49M
IT service management is one of the most universal functions in the modern enterprise and one of the most stubbornly resistant to meaningful automation. Existing platforms have digitized ITSM workflows, but the underlying work is still largely manual: tickets get logged, triaged, routed, and resolved through human effort organized by software rather than replaced by it. The result is that IT organizations spend enormous resources on resolution work that follows predictable patterns, and the data those resolutions generate is almost never used to prevent the next incident.
Standard Template Labs was founded by Amit Agarwal, former President and CPO of Datadog for over 13 years, who incubated the project at ICONIQ before spinning it out. The company is building an AI-first service management platform around its Axiom graph, a self-building knowledge structure that maps enterprise IT environments, learns how incidents arise and how they are resolved, and enables end-to-end automation of IT operations rather than merely organizing the people who perform them. The company launched in March 2026 with a $49 million seed round co-led by ICONIQ and CRV. Advisors include OpenAI CPO Arvind KC, former PayPal CTO Archie Deskus, and Datadog CISO Emilio Escobar. The ITSM market stands at $16.3 billion in 2026 and is growing at nearly 16% annually. For enterprises that have been running IT operations on human-organized software for a decade and want to run them on AI, Standard Template Labs is building the platform they have been waiting for.
This was a brief overview of the rapidly evolving AI agent platforms and frameworks landscape. If there is a company you think belongs on this list, reach out to our editorial team and we will make sure they are included on the next one.
