The market is moving beyond the chatbot.
At Google I/O 2026 and Google Cloud Next, Google described a shift toward “agentic” systems: AI that can do more than answer a question or draft text. Agents can be designed to navigate multiple steps, call tools, access connected systems and take actions within a workflow. Other major technology platforms are moving in the same general direction.
For business leaders, the important word is action.
The risk profile changes when AI goes from suggesting what a person should do to actually doing something. Reading an internal document is different from updating a customer record. Drafting an email is different from sending it. Recommending a purchase is different from placing an order.
Do not start with “We need an AI agent.”
Start with the business process. Is the workflow repetitive and well understood? Are the steps and exceptions documented? What systems would an agent need to access? What permissions are required? What is the consequence of a wrong action? Can the work be tested safely in a sandbox or approval-based process?
Data context is becoming a differentiator.
Google Cloud’s recent enterprise material emphasizes that agents need trusted business context and governed access to data. This is a broader lesson for any platform: an agent is only as useful as the information and permissions it receives. Poor context can lead to weak results; excessive permissions can increase risk.
Agentic AI increases the need for least privilege.
Give an AI system only the access needed for the task. Separate read, draft, recommend, approve and execute capabilities. For higher-impact actions, require human approval. Maintain logs that show what the agent did and why the workflow moved forward.
Evaluation also becomes more complex.
A content assistant can be tested on output quality. An agent needs to be tested on the full workflow: task completion, tool selection, permission handling, exception management, reliability, security and the business outcome. One impressive demo is not enough evidence for production.
The technology is advancing faster than most organizations’ operating models.
That is why readiness and governance matter. Organizations do not need to wait for a perfect policy, but they should know which categories of actions AI is allowed to take and how oversight increases with risk.
Most Canadian SMEs should begin with constrained agentic use cases.
Examples could include assembling information from approved sources, preparing a draft for human review, creating structured follow-up tasks, monitoring a defined queue or guiding an internal workflow. These are easier to control than fully autonomous customer, financial or operational decisions.
TridentTech perspective: agents make the ROAD approach more important, not less.
Readiness determines whether the workflow and systems are suitable. Opportunities identify where action creates enough value. The Action Plan defines permissions, guardrails and success measures. Delivery validates whether the agent performs reliably under real conditions.
The competitive advantage will not come from using “agents” as a label.
It will come from redesigning specific work so that AI and people each do what they are best suited to do, with clear accountability around the outcome.
Next step
Exploring AI agents or deeper workflow automation? Start by mapping the process, permissions, data and human approval points before selecting the platform.