Sydney’s BlueNexus Raises Funding to Expand AI Agent Management Platform
The M31 Capital-led round will support a platform that allows specialists and product teams to build, deploy and manage AI agents without coding.
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Sydney-based artificial intelligence startup BlueNexus has raised A$2 million (about US$ 1.3 million) in seed funding to expand its platform for building, deploying and managing AI agents.
The round was led by M31 Capital, with participation from Antler, Eastend Ventures and inSilico One, the company said in a statement. BlueNexus did not disclose its valuation.
The startup applies a vibe-coding approach to agent development through a plain-language builder and also provides the infrastructure needed to connect, operate and maintain the agents it creates. Users describe what they want an agent to do, after which the platform builds its personality, skills and scheduled tasks.
Users can then edit the configuration before publishing the agent. The company said its wizard can take an agent from an initial description to deployment in about 20 minutes.
The platform also provides persistent memory, knowledge bases and scheduled tasks, allowing agents to retain information across interactions and carry out work without waiting for a fresh prompt.
Agents can be deployed through web embeds, branded websites, email and services, including ChatGPT, Claude, Microsoft Copilot and Slack. Product teams can also integrate them into existing applications through BlueNexus’s application programming interfaces or a white-label widget.
BlueNexus offers more than 100 native connectors across 13 categories, linking agents to external applications and data sources. Users can also connect to external servers compatible with the Model Context Protocol (MCP), an open standard that enables AI systems to access external tools and data.
The startup is targeting domain specialists such as lawyers, accountants, clinicians, coaches and consultants seeking to turn their expertise and working methods into AI agents that clients can use.
It is also looking for software and product teams that want to embed agents in existing applications without separately building the supporting data, authentication, memory and deployment infrastructure.
BlueNexus says specialists can earn revenue each time their agents are used. Its website lists subscriptions, installation fees and marketplace revenue-sharing as forthcoming features, while the platform currently uses a credit-based, pay-as-you-go pricing model.
“Everyone talks about AI agents, but almost nobody with real expertise can actually ship one,” said Nick Arbuckle, the company’s chief operating officer. “We built the platform so that a lawyer, a coach or a fintech product team can go from an idea to a working, revenue-generating agent in the time it takes to have a coffee.”
For organisations handling sensitive information, BlueNexus offers confidential computing that runs agent workloads within a hardware-isolated trusted execution environment. The company said a SOC 2 audit was under way as the startup targets regulated sectors such as healthcare, legal services and finance.
BlueNexus was set up in August 2025 by Chief Executive Officer Chris Were, Chief Operating Officer Nick Arbuckle and Chief Technology Officer Aurélien Ticot. The company is run by a team of six.
The funding will be used to support the platform’s rollout, expand its connector ecosystem and pursue security and compliance certifications, the company said.
Sydney-based artificial intelligence startup BlueNexus has raised A$2 million (about US$ 1.3 million) in seed funding to expand its platform for building, deploying and managing AI agents.
The round was led by M31 Capital, with participation from Antler, Eastend Ventures and inSilico One, the company said in a statement. BlueNexus did not disclose its valuation.
The startup applies a vibe-coding approach to agent development through a plain-language builder and also provides the infrastructure needed to connect, operate and maintain the agents it creates. Users describe what they want an agent to do, after which the platform builds its personality, skills and scheduled tasks.
