Global fintech and funding innovation ecosystem

Montreal Jetty Raises $2M For Reliable AI Agents

May 13, 2026 | NCFA Fintech Market Activity | Artificial Intelligence And Data, Risk Compliance And Regtech

AI Image – AI workflow infrastructure

Agentic AI Needs Production Grade Controls

On May 6, 2026, Montreal based Jetty raised over $2 million in pre seed funding to build infrastructure for reliable agentic AI applications. AQC Capital and Hidden Layers Capital led the round. Mila Ventures, Akinox, and strategic angel investors with AI systems experience at Google and Meta AI also joined. While the round is early, the production problem is already very real.

Jetty is targeting the gap between AI agents that work in demos and agents that can handle enterprise workflows.  Jetty's platform gives agents structured runbooks, isolated execution environments and evaluation loops. The agent gets a defined job, runs in a controlled space, checks the result, and improves with human oversight. It's operating infrastructure for AI work that has to be repeatable, observable, and safe enough to review.

Jonathan Lebensold, Founder and CEO, Jetty

“Most AI systems today are still fragile - they work in isolation but break under real-world complexity,”

Demos Aren't Production Systems

Gartner predicts that over 40% of agentic AI projects will be cancelled by the end of 2027 because of rising costs, unclear business value, or weak risk controls. McKinsey’s 2025 global AI survey found that 23% of respondents are scaling agentic AI in at least one business function. Another 39% are experimenting.

These numbers explain Jetty's opening. Enterprises want to implement agentic AI, but they need proof before they let agents touch real workflows. Buyers need agents they can test, limit, monitor, correct, and review. This type of rigorous testing isn't optional in finance, insurance, health, and public services to name a few.

Doina Precup, Professor at McGill University and CIFAR AI Chair:

“As AI systems become more autonomous, ensuring they behave reliably in complex environments becomes a central challenge.”

Regulated Workflows Need Evidence

Financial institutions will only use and trust AI agents when the workflow transparently shows what happened, who approved it, and how mistakes get corrected.

Controls matter in onboarding, fraud review, compliance checks, and underwriting. They also matter in customer support, reporting, and internal operations. If an agent makes a mistake, teams need to see the inputs, how the tool used the inputs, the outputs, approvals, and any corrective actions.

The near term opportunity isn't just replacing staff with free running agents. It's reducing manual drag in workflows where humans still own the decision. That lines up with governed AI workflows in finance, where the value comes from evidence, reviewability, and accountability.

Canada has deep AI research talent, but productivity gains depend on companies that turn research into owned enterprise infrastructure. Reliable agent systems could become part of that. If Canadian firms build tools for evaluation, audit trails, controlled execution, and human review, they can own more of the AI workflow stack instead of only using tools built elsewhere.

What Jetty Still Has To Prove

This is still an early stage round, but the production problem is real. Jetty hasn't disclosed revenue, customer metrics, deployment volume, or reliability benchmarks. The company says it will use the funding to accelerate product development, expand engineering, and support enterprise customer deployments. That is the right use of proceeds, but the market will need proof that Jetty can make agents reliable in regulated workflows, not just promising in pilots.


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