Human API Launches Platform Letting AI Agents Hire Humans

has exited stealth with a platform designed to solve a growing bottleneck in AI deployment: the inability of autonomous agents to complete real-world tasks without human intervention. The company describes its product as the first agent-native system that allows AI agents to assign work directly to humans and receive structured output in return.

The platform introduces a new coordination layer where humans are treated as callable collaborators rather than external vendors. In practical terms, it gives AI systems a way to request judgment, perception, and physical-world interaction — areas where automation remains limited.

Human API was developed by Eclipse, the team behind an Layer 2 built on the Solana Virtual Machine. The project reflects a broader shift in how AI infrastructure is being designed: less around replacing humans, more around integrating them where machines still fall short.

Why AI agents still need humans

As AI agents become more capable, they increasingly run into tasks that are simple for people but inefficient or unreliable for machines. These include interpreting nuanced speech, capturing high-quality audio, exercising contextual judgment, or performing work that requires physical presence.

Until now, most AI systems handled these gaps through human-centric tools — outsourcing platforms, data vendors, or manual coordination pipelines. Those systems were never built for agents. They assume human operators on both sides and introduce friction when used programmatically.

Human API is designed to flip that model. Instead of forcing agents to adapt to human workflows, the platform allows humans to plug into agent-driven processes. Tasks are generated by AI systems, routed to people, reviewed, and paid out automatically.

Investor Takeaway

How the platform works

Human API functions as an agent-native execution and coordination layer. Human contributors create accounts, browse available tasks, accept assignments, and submit work. Tasks can include activities such as recording audio, labeling data, or performing context-sensitive actions.

Submissions pass through a review workflow before being approved. Once accepted, contributors are paid through Stripe Connect, providing a familiar and compliant payout mechanism.

For AI agents and companies, the platform offers a programmatic interface to high-quality human-generated output. Instead of negotiating with vendors or building custom pipelines, agents can request specific tasks and receive structured results at scale.

The design is intentionally narrow at launch. Human API is not trying to be a general freelancing marketplace. It is focused on repeatable, economically useful tasks that agents can request autonomously.

ahead use cases and business model

While in stealth, Human API contributed to the creation of a studio-quality audio dataset for a frontier AI lab. The dataset included nuanced speech characteristics such as accents and linguistic variation, demonstrating the platform’s ability to produce high-fidelity outputs that are hard to synthesize reliably.

Initial revenue will come from licensing audio data to AI labs. Over time, the company plans to expand into additional data types and task categories, including computer-usage data and work that requires real-world execution, such as logistics or on-site actions.

This expansion path mirrors how AI demand is evolving. As models move beyond text and images, the need for grounded, human-generated data grows. Platforms that can deliver that data efficiently stand to become core infrastructure rather than peripheral tools.

Investor Takeaway

Funding and the agent-native thesis

Human API has raised $65 million to date from investors including Placeholder, Hack, Polychain, DBA, and Delphi Ventures. The backing reflects confidence in a thesis that runs counter to much of the automation narrative: humans will remain economically relevant to AI systems for longer than expected.

The company frames its mission as a transition away from human-centric systems that treat agents as second-class users. Instead, it is building an environment where agents are first-class participants and humans are integrated as compensated collaborators.

If successful, the model creates a two-sided marketplace. AI companies gain access to reliable, scalable human input, while individuals can monetize skills that remain hard to automate.

Human API’s bet is that coordination, not intelligence, will define the next phase of AI development. As agents proliferate, the systems that allow them to act — not just think — may become just as valuable as the models themselves.