Enterprise AI Agents Move from Experiments to Infrastructure
Enterprise AI agents are software agents powered by large language models that autonomously coordinate tools, data, and workflows across an organisation to complete multi-step tasks, learn from outcomes, and share context over time. Investors are now treating these agents not as isolated features but as a new infrastructure layer that sits above existing systems of record. The latest funding for AI agent platforms shows a shift away from one-off chatbots or point tools toward orchestrated workflows that span departments and applications. Instead of every team adding its own AI widget, enterprises want workflow automation agents that can route tasks, call the right models, and reuse institutional knowledge. This is turning the “AI layer” into a shared service, where orchestration, security, and context management become central rather than optional.
Zaro.ai Bets on a Unified Context Layer for Enterprise AI
Zaro.ai has secured USD 5.1 million (approx. RM23.8 million) in pre-seed funding to build what it calls a single context layer for enterprise AI agents. Founded by Michael Bajwa and Qian Zheng, the startup wants to solve the fragmentation they saw when context, workflows, and outputs were scattered across disconnected AI tools. Zaro’s platform provides one workspace where company data, workflow automation agents, and custom applications share the same memory, owned by the business rather than a vendor. The system is model-agnostic, routing simpler tasks to cheaper models and reserving frontier models for complex work, a design the company says can cut costs by about ten times compared with frontier-only deployments. According to Cherry Ventures partner Dinika Mahtani, Zaro is “the first platform where AI demonstrably gets smarter the longer it operates inside an organisation.”
Orbio AI Targets Frontline Workforce Management with AI Agents
Orbio AI has raised £16 million in Series A funding to expand its AI agent platform for frontline workforce management. The company focuses on the 2.7 billion frontline workers it says are underserved by traditional enterprise software. Its suite of enterprise AI agents spans the full employee lifecycle: conducting interviews, assessing candidate fit, guiding onboarding, monitoring engagement, and spotting churn signals from the first application through to exit. Customers such as AWWG, Poke House, Atento, Yum Brands, and Adecco are already extending their deployments into new markets. Co-founder Sergi Bastardas says the goal is to compress hiring timelines to seconds and onboarding to hours instead of days, using coordinated agents rather than manual HR workflows. Dawn Capital partner Henry Mason notes that some large employers have rebuilt their operating models around Orbio within months, replacing long-standing labour budget lines.
From Point Solutions to Central AI Orchestration Layers
Both Zaro.ai and Orbio AI are responding to the same pain point: enterprises have adopted many isolated AI tools, but these rarely share context or coordinate work. Zaro.ai tackles this at a horizontal level, offering an AI orchestration layer that connects data, agents, and applications in one shared workspace. Orbio applies a similar logic vertically, using workflow automation agents to tie together hiring, onboarding, and retention processes for frontline staff. In each case, the platform becomes the place where AI agents are created, governed, and monitored, reducing fragmentation across teams and tools. This shifts value from individual AI features toward shared agent platforms that can standardise policies, reuse organisational knowledge, and enforce security. As more companies seek end-to-end automation, investor appetite for this central orchestration model is likely to grow.
