AI agent development for Canada

Agent, chatbot or automation?

The words get mixed up, so we separate them. A chatbot answers questions. A workflow automation follows fixed steps. An agent chooses its steps from a goal, calling tools as needed, such as looking up an order, drafting a reply, booking a slot and updating a record. That flexibility is useful, and it is also why agents need firmer limits.

We start with the simplest option that does the job. If a fixed workflow works, we build that, since it is cheaper and easier to test. We use agents where the path varies, such as triaging mixed enquiries or preparing research. Our AI agent vs chatbot comparison explains the difference in more detail.

Examples for Canadian businesses

Every agent gets the smallest set of tool permissions that lets it do its job. A reading agent cannot write, and a drafting agent cannot send.

Agents also need good data. If your price list lives in three spreadsheets that disagree, the agent will quote from the wrong one, so part of the project is often tidying the source before the agent reads it.

Safety, privacy and Canadian rules

• PIPEDA makes you accountable for personal information, including data sent to model providers. We document each data flow and choose settings and contracts with you.
• Quebec Law 25 asks for transparency about automated decisions affecting people, and a way to have them reviewed. We keep a person in the loop for decisions about individuals.
• CASL applies to commercial messages an agent might send. Agents draft; people or consent-aware systems send.
• Prompt injection is real: text in an email or web page can try to steer an agent. We separate instructions from content, restrict tools and test with hostile inputs. See the AI agent security checklist.

Planning notes, not legal advice. Your lawyer confirms what applies.

How we test an agent

Before launch we build a set of realistic tasks, including awkward ones: a customer who writes in French and English in one message, an invoice with a missing tax line, an email that tries to override the rules. We run them repeatedly and score the results. The agent ships only when it passes a bar you agree on, and we rerun the set after each change.

In production, every step is logged: the input, the tool calls, the output and who approved it. A weekly sample is reviewed by a person. If the agent starts failing, a kill switch returns the work to the team. The LLM evaluation checklist outlines the checks.

Cost control is part of testing. We measure how many model calls a typical task needs and set daily limits, so a loop or an abusive input cannot run up a large bill overnight.

Tools, models and hosting

We choose between OpenAI and Anthropic (Claude) models based on accuracy, cost and data terms for your task, and keep the code flexible so you can switch. Orchestration is done in n8n or in code, depending on complexity. Where location matters, workflows can run in a Canadian region, though a model API may still process data elsewhere, so we list each step plainly. Our build vs buy AI comparison helps you decide whether a custom agent is justified.

Project steps

1. Task selectionPick one task with clear success criteria, then a fixed quote within one working day.
2. PrototypeA working agent on sample data, with its tools in read-only mode.
3. PilotThe agent works alongside staff, with approvals on every action.
4. Widen carefullyPermissions expand step by step as the logs show reliable results.

Meetings fit Canadian mornings: 9:00 in Winnipeg is 20:30 in India in winter, and 9:00 in Toronto is 19:30.

Pricing

Projects start at $1,500 and our rate is $30 an hour, in US dollars. Tool access, testing depth and approval design move the price. Model usage is billed to you by the provider. See the AI agent development service page for scope, the AI agent cost guide for ranges, and the Canada AI overview for context.