Agentic AI takes centre stage in Canada’s next tech wave

Canada’s artificial intelligence sector is entering a new phase. After years of leadership in machine learning and generative AI, attention is increasingly shifting to “agentic AI”. These are systems capable not only of producing outputs but also of autonomously taking actions, coordinating workflows, and pursuing defined goals. This transition is already reshaping the country’s innovation strategy, enterprise landscape, and policy thinking.

From generative to agentic

Agentic AI builds on the capabilities of generative models but introduces autonomy. These systems can sequence tasks, interact with digital tools, and execute decisions within defined constraints. In practical terms, this means software that doesn’t just suggest actions but performs them — scheduling operations, managing supply chains, or even executing financial trades. Globally, this shift is viewed as the next major computing paradigm, with AI evolving from passive tool to active collaborator.

Canada, already home to world-leading AI research institutions, is well positioned to capitalise on this transformation. For instance, the Canadian government has moved decisively to embed AI, including agentic systems, into economic and public-sector priorities. In June 2026, Ottawa launched its new national strategy, “AI for All,” committing billions of dollars to scale adoption and strengthen domestic capabilities. The plan aims to deliver substantial economic gains, targeting up to $200 billion in GDP growth and 250,000 new jobs over five years.

Policymakers recognise that agentic AI introduces new risks. Because these systems can act autonomously, governance frameworks must extend beyond traditional AI oversight, incorporating stronger controls around accountability, auditability, and unintended system interactions. This reflects a broader shift: innovation is now being paired with a “safety-first” approach, grounded in privacy, transparency, and public trust.

Canada’s strength lies in its integrated AI ecosystem. Cities such as Toronto, Montreal, and Vancouver combine academic excellence with startup activity and enterprise adoption. Plus, institutions like Mila, the Vector Institute, and Amii continue to produce cutting-edge research while feeding talent into commercial ventures. This foundation is now supporting a rise in agentic AI companies developing autonomous systems for real-world applications. Canadian firms are deploying AI agents in sectors ranging from healthcare diagnostics and logistics optimisation to cybersecurity and financial modelling.

The impact of agentic AI is becoming visible across industries. In cybersecurity, agent-based platforms are now capable of conducting investigations autonomously, reducing the need for manual intervention.

In retail and finance, AI agents are beginning to execute transactions on behalf of users — from purchasing goods to managing investment portfolios — albeit with safeguards to limit risk.

Manufacturing and logistics are also being transformed. Agentic systems can manage complex workflows, dynamically adjust supply chains, and optimise production processes in real time.

Government investment is reinforcing this growth. In May 2026, for example, nearly $16.5 million was allocated to AI-focused businesses in the Greater Toronto Area to accelerate commercialization and adoption.

Challenges and caution

Despite the momentum, significant hurdles remain. Research suggests that current AI agents still struggle with reliability, achieving professional-level task completion only a small fraction of the time. [cbc.ca] This gap between potential and performance is fuelling debate. While some organisations are investing heavily in agent-based systems, others warn of overhype and the risk of premature deployment.

Agentic AI is powerful but its autonomy introduces new risks in control, security, reliability, and accountability, requiring stronger governance, monitoring, and human oversight than previous AI systems.

Trust will be critical. For agentic AI to reach mainstream adoption, users must be confident that these systems can act safely, transparently, and in alignment with human intent. The result is a national ecosystem attempting to do two things simultaneously: lead in technological capability while setting global standards for trustworthy AI.