Infrastructure, LLMOps tooling, runtime security, and governance enabling trustworthy agentic AI (including early-stage bets)

The ecosystem is rapidly building the foundational stack necessary for production-grade, trustworthy agentic AI—a critical leap toward large-scale deployment across industries. This emerging infrastructure layer encompasses advanced orchestration platforms, robust LLMOps tooling, comprehensive observability, runtime security, and compliance mechanisms, all designed to support autonomous systems operating reliably and securely at enterprise scale.

Building the Core Infrastructure for Agentic AI

Recent months have seen a surge in investment and innovation aimed at establishing resilient, scalable infrastructure. Companies like Portkey and Union.ai are leading the charge:

• Portkey, a pioneer in multi-agent orchestration, recently completed a $15 million Series A led by Elevation Capital. Its platform emphasizes policy enforcement and safety boundaries, enabling enterprises to deploy agents that operate within strict safety and compliance constraints—an essential foundation for trustworthy autonomous systems.

• Union.ai secured $38.1 million in Series A funding to develop scalable AI workflow management infrastructure, supporting the deployment and monitoring of complex AI systems at enterprise scale.

• Inscope raised $14.5 million to develop enterprise data workflows that underpin decision-making processes, ensuring AI decisions are traceable and reliable.

• Leading platforms like Basis are exemplifying operational maturity, managing billions of agent work units and processing 20 trillion tokens annually, with $800 million ARR, demonstrating widespread enterprise trust.

Additionally, startups such as Venice are innovating in runtime security and privileged access management (PAM):

• Venice raised $33 million to develop adaptive privilege systems that dynamically adjust agent privileges based on contextual signals, enforcing least-privilege principles and reducing attack surfaces during agent operations.

This infrastructure development ensures that autonomous agents are built on secure, scalable, and compliant foundations, enabling their safe integration into enterprise workflows.

Observability, Security, and Regulatory Compliance

As autonomous agents become central to enterprise operations, security and compliance are now core operational pillars:

• Real-time observability capabilities are being embedded into LLMOps platforms like Portkey, supporting performance analytics, behavior monitoring, and anomaly detection. This allows organizations to continuously oversee agent behaviors, promptly detect deviations, and intervene proactively.

• Behavior anomaly detection systems from firms like Darktrace and Zast.AI monitor agent activities for unexpected behaviors or threats, enabling preemptive security responses.

• Retrieval-Augmented Generation (RAG) techniques are increasingly integrated into systems like Inscope, providing audit trails, decision provenance, and regulatory reporting—crucial for trustworthiness and regulatory adherence.

• Regulatory standards such as ISO/IEC 42001:2023 and the EU AI Act are shaping system design. Organizations now adopt compliance-as-code approaches, embedding safety checks, audit logs, and policy enforcement directly into AI systems, allowing rapid, compliant deployment.

This focus on runtime security, anomaly detection, and compliance underscores a maturing understanding that trustworthy autonomous AI requires layered security and transparent governance.

Early-Stage Bets and Quiet VC Activity

Beneath the headline innovations, a wave of quiet venture capital activity signals strategic bets on the infrastructure that will underpin the agent economy:

• Dyna.Ai raised an eight-figure Series A to advance its agentic AI platform, targeting enterprise automation and decision-making.

• Tess AI secured $5 million to develop agent orchestration solutions for automating complex workflows.

• Guild.ai raised $44 million in seed and Series A funding, focusing on building reliable, governance-ready AI agents.

• Potpie AI secured $2.2 million pre-seed to create context-aware development tools for AI integration.

These early investments reflect a recognition that building a layered, trustworthy AI infrastructure is critical for enabling scalable autonomous systems across sectors.

The Path Forward

The convergence of investment, technological innovation, and regulatory maturation signals a clear trajectory: trustworthy agentic AI is transitioning from experimental to operational. Enterprises demand systems that are secure, transparent, compliant, and capable of operating reliably in complex environments.

Key priorities include:

• Developing robust observability and security layers that support real-time monitoring and anomaly detection.
• Embedding compliance frameworks aligned with ISO/IEC 42001 and the EU AI Act directly into AI lifecycle processes.
• Enabling adaptive privilege management to reduce security risks during agent operations.
• Supporting supply chain security and regulatory reporting to meet evolving norms.

Conclusion

The infrastructure layer underpinning trustworthy agentic AI is rapidly solidifying, driven by strategic investments and pioneering startups. As organizations build these resilient foundations, they are better positioned to deploy large-scale, autonomous AI systems that operate with integrity, security, and compliance—paving the way for a future where agentic AI becomes an indispensable, trustworthy component of enterprise and societal infrastructure.