AI Agent Development for Canadian Businesses That Ships Past the Demo
An AI agent is software that uses an LLM to plan, call your tools, and complete a multi-step task with limited supervision. MicroPyramid builds custom AI agents, agentic workflows, and multi-agent systems for Canadian startups and SMBs — grounded in your data, wired into your systems, and backed by the evaluation, guardrails, and monitoring that most demos skip.
Every agent is designed around PIPEDA, the provincial privacy laws, and Quebec Law 25's automated-decision disclosure duties that are already in force. We deploy on AWS ca-central-1 (Montréal) when data residency matters, bill in CAD via Stripe with GST/HST-compliant invoicing, and review agent behaviour live every Eastern Time morning — then keep building through your night.
Why Canadian Teams Build Agents With Us
Four things Canadian founders and CTOs consistently ask about before trusting a partner with an agent that acts inside their systems
ET Mornings Live, Progress Overnight
Eastern Time mornings align with the end of our working day, so stand-ups, sprint reviews, and agent-behaviour reviews happen live while your team is fresh. The rest moves async — we keep building through your night and hand off progress with clear end-of-day notes, so you wake up to a further-along agent, not a status request.
PIPEDA and Provincial Privacy-Aware Agent Design
Agents touch more data and take more actions than chatbots, so we design for PIPEDA and the provincial regimes — Quebec Law 25, BC and Alberta PIPA — from day one: data minimisation in prompts, scoped tool permissions, audit-ready logging of every action, and breach-response design that fits PIPEDA’s breach-reporting rules. The OPC has published principles for generative AI; we build to the standard it is signalling.
Automated Decisions, Already Regulated in Quebec
Quebec’s Law 25 already requires you to tell people when a decision about them is made exclusively by automated processing — with rights to an explanation, the data used, and a human who can review it. Our agents ship with the decision inventory, human checkpoints, and logging that make those duties straightforward, in Quebec today and wherever federal rules land next.
CAD Billing, Senior Ownership, Your Code
Invoices in CAD via Stripe with GST/HST-compliant invoicing on request, a fixed estimate after a short discovery sprint, every build owned by a senior engineer, and all source code, prompts, and eval suites committed to your repositories as we work — no lock-in.
AI Agent Development Services for Canadian Teams
From a single task-completing agent to coordinated multi-agent systems — with the grounding, guardrails, and evaluation that make them safe to run
Custom AI Agents
Task-completing agents that plan, call your tools and APIs, and finish multi-step work — not just chat back at the user. Designed PIPEDA and Law 25-aware from the first prompt.
- Goal-driven planning & reasoning
- Tool / function calling
- Human-in-the-loop checkpoints
Agentic Workflow Automation
Replace brittle manual or rules-based processes with agents that read context, decide, and act — the document-heavy back-office work common in Canadian professional services and financial services.
- Document & ticket triage
- Research and data gathering
- Back-office process automation
Multi-Agent Systems
Coordinated agents that split a complex job into specialised roles — planner, researcher, executor, reviewer — with shared state and clear handoffs.
- Orchestration & routing
- Specialised sub-agents
- Shared memory and state
Tool & System Integration
Wire agents safely into the systems they act on — your APIs, databases, SaaS tools, and Canadian staples like QuickBooks, Sage, helpdesks, and CRMs — with scoped, auditable permissions.
- API & MCP tool servers
- CRM, ERP & helpdesk hooks
- Scoped, auditable permissions
RAG-Grounded Agents
Agents that retrieve from your own documents and data before they act, so answers and decisions stay grounded in fact — with your data and vector store kept in-region on ca-central-1 (Montréal).
- Vector search over your data
- Source citations
- Reduced hallucination risk
Evaluation, Guardrails & Monitoring
The part most demos skip — measuring whether the agent is actually correct, safe, and cost-controlled in production, with the audit trail the OPC, Quebec’s CAI, and OSFI expect.
- Eval suites & test cases
- Guardrails and fallbacks
- Tracing, cost & latency monitoring
The Canadian Rules That Actually Apply to AI Agents
There is no Canadian AI Act — agents are governed by privacy law you already know, one set of duties already in force in Quebec, and sector deadlines. Here is the honest picture, as of mid-2026
PIPEDA & Provincial Privacy Laws
What binds todayThere is no Canadian AI Act — AIDA died with Bill C-27 when Parliament was prorogued in January 2025, and its replacement has not been tabled. AI agents that handle personal information are governed by PIPEDA, enforced by the Privacy Commissioner of Canada, alongside Quebec Law 25 (enforced by the CAI) and the BC and Alberta PIPAs — with the OPC’s published principles for generative AI signalling how it expects AI systems to behave.
- The OPC is the federal enforcing regulator
- Quebec’s CAI enforces Law 25
- PIPEDA breach reporting covers agent incidents
- Data minimisation in prompts and logs
Quebec Law 25: Automated-Decision Duties
Already in forceIf you serve Quebec residents, Law 25 already obliges you — since September 2023 — to inform a person when a decision about them is based exclusively on automated processing, no later than when you tell them the decision. On request you must explain the principal factors, show the personal information used, and give them a route to make observations to a human who can review the decision. Canada’s automated-decision rule is not coming; in Quebec, it is here.
- Disclose exclusively automated decisions
- Right to the reasons and the data used
- Right to a human who can review the decision
- We inventory agent decisions in discovery
Sector Rules & the Federal Gap
Where it gets stricterThe federal AI for All strategy (June 2026) is investment, not regulation — a new “light, tight” AI bill is promised but not yet law. Sector rules are firmer: OSFI’s Guideline E-23 brings AI and machine-learning models at federally regulated financial institutions under model-risk management from 1 May 2027, and the federal Directive on Automated Decision-Making already binds government institutions. The EU AI Act only reaches you if you place systems on the EU market or use their outputs in the EU.
- OSFI E-23 model-risk rules from 1 May 2027
- Directive on ADM binds federal government systems
- AI for All strategy = investment, not regulation
- EU AI Act — only when serving the EU
The practical takeaway: most Canadian agent projects do not need new compliance machinery — they need the agent designed so data minimisation, scoped permissions, human checkpoints, and audit logging are built in from the start, with the agent's significant decisions inventoried wherever Quebec's Law 25 disclosure duty — or OSFI's 2027 model-risk deadline — applies. That is how we build by default, and we map exactly which rules apply to your workflow during the discovery sprint.
Where an Agent Earns Its Keep
If any of these match where your Canadian team is, an agent is probably worth a conversation
Customer Support Teams
You want an agent that resolves common tickets end-to-end — reading the account, checking systems, and taking action — with escalation to a human when unsure. Common in Canadian SaaS and e-commerce support queues.
Operations & Back Office
You have repetitive multi-step workflows — triage, data entry, reconciliation, research — that rules engines never quite handled and your Canadian ops team finds tedious.
Professional Services Document Work
Canadian accountancies, legal teams, insurance brokers, and agencies drowning in contracts, case files, invoices, and client email. Agents extract, classify, draft, and route — with a person approving the output.
SaaS Teams Adding Agents
You want to ship an in-product copilot or autonomous workflow as a feature of your Canadian SaaS product, and need engineers who can make it reliable for real users.
Teams With a Failed POC
You built an agent demo that impressed in a meeting but broke on real data, cost too much, or could not be trusted in production. We rebuild around evaluation first.
Regulated & Fintech Teams
You operate under OSFI or provincial-securities oversight or handle sensitive data, and need agents with audit trails, human checkpoints, and data handling you can defend to a regulator — not a black box.
Best Fit For
- teams with a real multi-step task to automate — not just a chatbot that answers FAQs
- Canadian startups and SMBs adding an agent or copilot as a product feature or internal tool
- teams that need the agent grounded in their own data, tools, and permissions — with PIPEDA and Law 25-compliant handling throughout
- teams that want evaluation, guardrails, and monitoring — not a demo that breaks in production
Not the Right Fit When
- a static FAQ bot with no actions, where a simple RAG assistant is the better fit
- fully autonomous, unsupervised control over high-risk actions with no human checkpoints
- "add AI" as a marketing slogan with no concrete task, data, or workflow behind it
- expectations of 100% accuracy with zero evaluation, oversight, or fallback design
If you need a grounded assistant or doc search rather than actions, see AI / RAG Knowledge Systems, or AI Feature Development to embed one capability in your product.
Custom Agent, Off-the-Shelf Copilot, or No-Code?
The honest version of the trade-off — so you only invest in a custom build when it actually pays off
Off-the-shelf copilot
Generic assistance fast — drafting, summarising, Q&A inside tools you already pay for.
Cannot act inside your systems, no access to your private data or workflows, and you cannot tune accuracy or prove PIPEDA-compliant handling of what it sees.
Pick when the need is general productivity, not a task specific to your business.
No-code agent builder
A quick first workflow without engineers, useful for prototyping and simple internal automations.
Hits a wall on real integrations, permissions, evaluation, and cost control; hard to debug when it misbehaves — and hard to evidence the decision flow when Quebec’s Law 25 disclosure duties or a regulator ask how a decision was made.
Pick for low-stakes internal experiments where occasional errors are acceptable.
Custom-built agent (what we do)
Built around your task, grounded in your data, wired into your tools, evaluated, monitored, and designed for PIPEDA and Law 25’s automated-decision duties from the start.
Needs engineering investment up front — worth it when the workflow is core, sensitive, or high-volume.
Pick when the agent touches real systems, real data, or real customers and has to be trusted.
How We Build an Agent You Can Trust
Reliability comes from the order of operations — task, rules, and evaluation first, autonomy last
Pin Down the Task
We define the specific task, the systems involved, what "good" looks like, and which Canadian rules apply — PIPEDA and the provincial laws, Law 25’s automated-decision duties, OSFI expectations — before writing agent code.
Prototype the Loop
We build the smallest working agent loop against real data and tools, so you see real behaviour early — reviewed live in your ET morning overlap, not a scripted demo.
Ground, Integrate & Guard
We add retrieval over your data, tool access with scoped permissions, human checkpoints, and guardrails — with data minimisation and audit-ready logging designed for OPC and CAI expectations.
Evaluate & Ship
We measure accuracy and cost against a test suite, add tracing and monitoring, then ship in stages with a human in the loop — autonomy is earned, not assumed.
AI Agent Technology Stack
Model-agnostic by design — we pick the model, framework, and data layer that fit your task, budget, and Canadian data-residency needs, deploying to AWS ca-central-1 (Montréal) by default
Models
Orchestration & Retrieval
Engineering & Ops
How Canadian Teams Get Started
We recommend starting with an Agent Discovery Sprint — confirm an agent is the right tool, and which Canadian rules apply, before committing to a build. Fixed estimate after discovery, billed in CAD.
Agent Discovery Sprint
Clarify the task, data, tools, risks, and which Canadian rules apply — and confirm an agent is the right tool before committing to a build. Fixed estimate at the end, in CAD.
- Use-case & feasibility review
- Data, tool & compliance inventory
- Architecture & guardrail plan
- Clear delivery roadmap
Agent Pilot Build
Ship one working agent against real data and tools, with evaluation and a human-in-the-loop, ready to trial with your Canadian users.
- One end-to-end agent
- Real integrations & retrieval
- Eval suite & guardrails
Agent Scale & Operate
Harden a working agent for production and expand it — more tools, more workflows, monitoring and cost control, billed in CAD.
- Production hardening
- New tools & workflows
- Monitoring, retainer or T&M
Recent Work and Portfolio
Products we have built and shipped for startups and SMBs — including AI-assisted platforms like Refactored.ai and workflow-heavy products in regulated contexts.
Refactored.ai
AI-assisted Python learning platform with interactive tutorials, AI feedback, and automated assessment — direct AI delivery proof.
Read case study
CREDITABLE
Employee financial wellness platform for savings, loans, and workplace finance — workflow-heavy delivery in a regulated context, relevant to Canadian fintech.
See client story
Bough Digital
Digital marketing platform — campaign management, analytics, and workflow-heavy product delivery for an overseas client across timezones.
See more work
PRO Music Tutor
Online music learning platform connecting students with world-class instructors.
See portfolioFrequently Asked Questions
Straight answers to what Canadian founders and CTOs ask us before building an agent.
What is an AI agent?
An AI agent is software that uses a large language model to plan and complete a multi-step task with limited supervision — it decides what to do, calls tools or APIs to take real actions, observes the result, and continues until the task is done. Unlike a chatbot that only replies with text, an agent can read context, retrieve data, and act inside your systems.
How is an AI agent different from a chatbot or a RAG assistant?
A chatbot answers questions in text; a RAG assistant answers questions grounded in your documents; an AI agent goes further and takes actions — calling tools, updating records, or running a multi-step workflow to actually complete a task. Many real systems combine all three: retrieval to stay grounded, conversation for the interface, and agentic tool-calling to get work done.
Does Canada have an AI law that applies to AI agents?
No — as of mid-2026 Canada has no AI Act. The proposed Artificial Intelligence and Data Act (AIDA) died with Bill C-27 when Parliament was prorogued in January 2025, and the federal AI for All strategy launched in June 2026 is an investment plan, not regulation — a new AI bill is promised but has not been tabled. In practice, AI agents that handle personal information are governed by PIPEDA, enforced by the Privacy Commissioner of Canada, alongside Quebec Law 25 and the BC and Alberta PIPAs, with sector regulators like OSFI applying their own rules and the federal Directive on Automated Decision-Making binding government institutions. The EU AI Act only applies to Canadian businesses that place AI systems on the EU market or use their outputs in the EU.
Can an AI agent lawfully make automated decisions about people in Canada?
Yes — no Canadian law prohibits automated decision-making, but in Quebec the transparency duties are already in force. Since September 2023, Law 25 requires you to inform a person when a decision about them is based exclusively on automated processing, no later than when you communicate the decision, and on request to explain the principal factors, show the personal information used, and let them make observations to a human who can review it. Federally regulated financial institutions also face OSFI’s Guideline E-23 model-risk rules from 1 May 2027. We inventory which of an agent’s decisions cross these thresholds during discovery and design the disclosure, human-review checkpoints, and logging in from the start.
Where is our data processed, and can it stay in Canada?
When Canadian data residency matters, we deploy on AWS ca-central-1 (Montréal) and keep your application data and vector store in-region, with prompts minimised so third-party LLM API calls carry only what the task needs. PIPEDA does not mandate onshore-only hosting, but it holds you accountable for personal information sent across borders, and Quebec Law 25 requires a privacy assessment before personal information leaves Quebec — and we are honest that a Canadian region of a US cloud provider remains subject to the US CLOUD Act. Where that matters, we can self-host open models on Canadian infrastructure so data never leaves the country, and we design the data flow so you can answer exactly where your data goes.
When should we build a custom agent instead of using an off-the-shelf copilot?
Use an off-the-shelf copilot for general productivity like drafting and summarising. Build a custom agent when the task is specific to your business, needs access to your private data and systems, must follow your permissions and audit rules, or has to satisfy PIPEDA obligations and Quebec Law 25’s automated-decision disclosure duties — things generic copilots and no-code builders cannot do reliably.
How do you stop an AI agent from hallucinating or taking wrong actions?
We ground the agent in your real data with retrieval and citations, scope its tool permissions so it can only do safe things, add human-in-the-loop checkpoints before high-risk actions, and build an evaluation suite that measures accuracy on real cases before launch. Guardrails, fallbacks, and production monitoring catch the rest — this evaluation layer is what separates a reliable agent from a demo, and it also produces the audit trail the OPC, Quebec’s CAI, and OSFI expect.
How long does it take to build a working AI agent, and what drives the cost?
A focused agent pilot against real data and tools typically ships in weeks, not months. Cost is driven by scope: how many systems the agent must integrate with, how sensitive the data is, whether Law 25’s automated-decision duties or OSFI expectations apply, and how much evaluation the risk level demands. We give you a fixed written estimate after a short discovery sprint and bill in CAD via Stripe with GST/HST-compliant invoicing, so you decide before committing.
Why work with MicroPyramid instead of a Toronto or Vancouver agency or building in-house?
Build in-house when you already have senior AI engineers with spare capacity. A local Toronto or Vancouver agency gives you on-site workshops at a local cost base — and is the right call for security-cleared or Protected B government work that must stay onshore. We offer a third path: a senior team that has shipped production software for 12+ years, live overlap every Eastern Time morning with async end-of-day handoffs so the agent moves forward overnight, PIPEDA and Law 25-aware delivery with AWS Montréal (ca-central-1) residency, CAD billing, and an evaluation-first build process — usually at a fraction of the local cost structure.
Do we own the agent and the code?
Yes. You own all source code, prompts, evaluation suites, and intellectual property we produce. Everything is committed to your repositories as we build, with no lock-in, so you can run, extend, or bring the work in-house at any time.
Turn a Workflow Into a Working Agent
Bring us a real task — support resolution, back-office automation, document workflows, or an in-product copilot — and we will tell you honestly whether an agent fits, map the Canadian rules that apply, and build one you can trust in production.