Exclusive | OpenAI Backs New AI Startup Seeking Bot Army Breakthroughs - WSJ

OpenAI has backed a San Francisco startup called Isara with $94 million in funding at a $650 million valuation, according to the Wall Street Journal. The company aims to build software to coordinate thousands of AI agents working in concert on complex tasks — and if that sounds familiar, it should. Multi-agent orchestration is the hottest infrastructure thesis of 2026, and every serious player is placing a bet.

The question worth asking is whether Isara has a real answer to the coordination problem, or just a well-funded announcement with a demo.

The founders and what they built before Isara

Henry Gasztowtt, an Oxford University computer science graduate student who serves as Isara's CEO, co-authored a 2024 arXiv paper called "Large Legislative Models" with Edwin Zhang. The paper explored how AI systems might cooperate to improve policymaking — an academic exercise, but one that touches directly on the coordination challenge Isara is now trying to commercialize.

Edwin Zhang, Isara's co-founder and chief scientist, dropped out of a Harvard PhD program to join OpenAI's safety team, where he worked on systems to supervise complex model behaviors and early prototypes of multi-agent coordination. He also co-authored a paper called "Transcendence" with Vincent Zhu, published at NeurIPS 2024, which demonstrated that generative models can in some cases outperform the experts that trained them — a result that suggests a reason to care about agent coordination, not just individual agent capability.

This is not two college students with a pitch deck. Zhang's OpenAI background is specifically relevant: he built the multi-agent prototype work that Isara is now trying to turn into infrastructure. That background is the reason OpenAI is writing a $94 million check.

The demo and what it actually showed

Isara has demonstrated its approach by coordinating approximately 2,000 AI agents to forecast the price of gold, according to multiple reports. The company has also disclosed plans to expand to finance, biotech, and geopolitics applications, and is building infrastructure for agent networks to collaborate on customer support, commerce automation, and internal process handling.

Here is the problem with the demo: gold price forecasting is the canonical "we have no product yet" demonstration in agentics. It is visually impressive, it involves enough variables to sound substantive, and critically, nobody publishes accuracy numbers for public reference. Isara has shown that it can run 2,000 agents in coordination. It has not shown that the coordination produces correct results faster, cheaper, or more reliably than alternatives. There are no accuracy figures, no methodology disclosures, and no public product available for inspection.

This is not unusual for a company at Isara's stage. But it matters when the headline number is a $650 million valuation, which is a bet on the thesis, not the code.

Isara has recruited more than a dozen researchers from Google, Meta, and OpenAI, which is a credible signal that something technically serious is being built. The team quality is real. Whether the infrastructure underneath is novel or a LangGraph workflow with better branding is a question the public record does not yet answer.

The coordination problem, briefly explained

Getting one AI agent to complete a task is hard enough. Getting thousands to work together on a shared problem — dividing labor, sharing partial results, resolving conflicts, maintaining coherent state — is an open engineering problem. Existing frameworks like LangGraph, AutoGen, and CrewAI handle coordination for small numbers of agents in relatively constrained workflows. Scaling to hundreds or thousands of agents coordinating simultaneously, with coherent task decomposition and result aggregation, is where the engineering gap currently sits.

Isara's claim is that it has solved that gap. Its academic origins (the Large Legislative Models paper) suggest a specific approach: agents that can negotiate roles, share partial conclusions, and revise collective strategy mid-task, rather than following a pre-configured workflow. If that architecture is real and stable, it would be genuinely novel. If it is a sophisticated prompt engineering layer over an existing orchestration framework, it would not be.

The distinction matters. The agent infrastructure space has seen a wave of "coordination layer" announcements where the underlying technology is a thin wrapper around LangChain or LangGraph, marketed with the language of paradigm shift. Reading the source is the only way to know, and the source has not been published.

What to watch

Three things will determine whether Isara justifies its valuation. First, whether the company publishes technical documentation — a paper, a public repo, a changelog — that shows what the coordination protocol actually does. A $650 million valuation buys the market approximately eighteen months of runway at typical AI startup burn rates; if there is real infrastructure, it should be visible before the next fundraise conversation.

Second, the accuracy question. A multi-agent gold price forecast is only interesting if it is more accurate than a single-agent approach or a simpler ensemble. Isara has not published accuracy data, and external verification is not possible without access to the system. That will change when the company takes on paying customers or publishes benchmark results.

Third, the OpenAI relationship. Zhang's background gives OpenAI a specific window into what Isara is building. The investment terms are not public, but the pattern — a former researcher taking a thesis from the lab that funded them into a funded startup — is a well-worn path in AI. The question is whether OpenAI's stake reflects a strategic interest (integration, distribution, research alignment) or a financial one.

Isara is not the first company to claim it has solved multi-agent coordination at scale. It may be the first to have $94 million and a team credible enough to actually try. Whether that attempt produces novel infrastructure or just another wrapper around an existing framework is the only question that matters for builders and investors watching this space.