Platforms for AI infrastructure, observability, agent runtime and data workflows

Platforms for AI Infrastructure, Observability, Agent Runtime, and Data Workflows in 2026

The rapid advancement of embodied AI and autonomous systems in 2026 hinges critically on the development of sophisticated platforms that support scalable infrastructure, robust observability, secure runtimes, and efficient data workflows. This year marks a pivotal shift toward specialized tools and ecosystems designed to ensure trustworthy, resilient, and regionally sovereign AI deployment across sectors ranging from healthcare to space exploration.

Funding and Growth in Core AI Infrastructure Startups

A key driver behind these developments is the influx of capital into startups building foundational AI infrastructure:

• Observability and Monitoring: Companies like Braintrust have secured significant funding—an $80 million round—to establish themselves as the observability layer for AI. These platforms are crucial for real-time system monitoring, debugging, and ensuring operational transparency, especially as AI systems become more complex and embedded in critical infrastructure.

• LLMOps and Data Infrastructure: Startups such as Portkey (raising $15 million) and Union.ai (completing a $38.1 million Series A) are addressing the needs of large language model operations (LLMOps) and data workflow automation. These tools streamline model deployment, versioning, and lifecycle management, enabling organizations to scale AI solutions confidently.

• Data Infrastructure and Storage: Companies like Eon, which raised $300 million led by Elad Gil, focus on unlocking "AI data goldmines" through innovative cloud infrastructure that supports high-throughput data processing essential for training and inference.

Tools for Running, Monitoring, and Scaling AI Agents and Backends

In tandem with core infrastructure, a vibrant ecosystem of tools is emerging to facilitate the deployment, management, and security of AI agents in production environments:

• Agent Runtime Platforms: Tensorlake offers a developer platform that enables teams to run AI agents at scale without managing underlying infrastructure, supporting complex document processing workflows and multi-agent orchestration. Such platforms are vital for deploying embodied AI in robotics, autonomous vehicles, and complex operational environments.

• Security and Confidential Compute: As AI systems permeate sensitive sectors, confidential compute solutions—from Opaque Systems to Trusted Execution Environments (TEEs)—are becoming mainstream. These technologies safeguard private data during inference and reasoning, ensuring compliance and trustworthiness.

• Observability and Security Tools: Platforms like Braintrust and OpenAI’s Claude are integrating “Remote Control” features, allowing operators to monitor, audit, and intervene in AI systems in real-time. Complementary tools from startups like Reco and organizations such as Mozilla are developing vulnerability detection and adversarial attack mitigation tools, reinforcing the security and resilience of embodied AI.

• AI Monitoring for Network and Data Workflows: Selector, an AI-infused network observability startup that raised $32 million, exemplifies the trend toward holistic system observability, ensuring AI infrastructure remains transparent and reliable amid increasing complexity.

Supplementing Infrastructure with Developer and Deployment Tools

To accelerate AI adoption, new platforms are empowering developers with regionalized, secure, and user-friendly environments:

• Multi-Agent Development Environments: Tools like Superset IDE enable local multi-agent AI development, fostering sovereignty and compliance while boosting productivity.

• Remote Management and Continuous Operation: Solutions such as MaxClaw by MiniMax provide persistent, managed agents capable of continuous operation 24/7, suitable for critical infrastructure and high-stakes automation.

• Sovereign Deployment Strategies: Regional infrastructure initiatives—like Europe’s Mistral acquiring Koyeb or Alibaba’s deployment of Qwen3.5—are expanding the reach of autonomous AI ecosystems, ensuring regional sovereignty and resilience in data and compute.

The Future of AI Infrastructure and Observability

The convergence of massive funding, hardware innovation, and security frameworks is laying the foundation for trustworthy, scalable, and sovereign AI systems. The deployment of confidential compute, cryptographic validation, and advanced observability platforms is critical for regulatory compliance and public trust, especially as embodied AI systems become integral to sectors like healthcare, defense, and space exploration.

Looking ahead, continued investments in multi-agent local development environments, regionally distributed infrastructure, and security-enhanced runtimes will be essential. These platforms will enable embodied AI to operate safely, reliably, and within regional sovereignty constraints—paving the way for a future where autonomous agents are trusted partners in societal progress, economic stability, and interplanetary exploration.