AI Agent Development Company in Calgary

We quote against scope, because the cost driver is the number of systems the agent touches and the evidence standard its output has to meet, not the model. A discovery and feasibility engagement, covering workflow observation, a data and tool inventory, a governance perimeter, an evaluation design and an honest recommendation on whether an agent is the right answer at all, typically runs CAD 35,000 to 80,000 over four to six weeks. A production agent for a single workflow, meaning real integrations, retrieval over a governed document set, approval gates, an action log, an evaluation suite and handover documentation, typically runs CAD 120,000 to 400,000. A multi-workflow rollout across several teams with shared retrieval infrastructure, identity integration and an internal platform other teams can build on runs CAD 400,000 to 1.2 million and up. Inference and cloud costs sit outside those figures and stay in your name, and we model them per workflow before build so nobody is surprised by the first full month. Novel agent engineering often qualifies for the federal Scientific Research and Experimental Development programme, which pays an enhanced refundable credit to Canadian-controlled private corporations up to an annual expenditure limit and a lower non-refundable rate above it; we supply time-tracked technical narratives, and eligibility is your SR&ED adviser's call, not ours.

No federal statute, and any vendor telling you otherwise is wrong. The Artificial Intelligence and Data Act was part of Bill C-27 and died on the order paper when Parliament was prorogued in January 2025; it was never enacted and is not in force. What actually constrains an agent is existing law plus the obligations the agent's output inherits. Alberta's Personal Information Protection Act and PIPEDA govern personal information the agent collects, uses or discloses, including anything that ends up in a prompt or a retrieval index. Human rights and employment law govern outcomes that affect people. Where the agent's output feeds a regulatory submission, the operator's existing traceability and reporting obligations apply to that output exactly as they would to a spreadsheet, which is why we log the source data, the retrieval hit, the model output and the approving human for every drafted filing. Federally regulated financial institutions carry OSFI's model risk expectations, and the revised Guideline E-23 published in September 2025 explicitly reaches machine learning models and takes effect in 2027, so institutions are building model inventories now. Quebec's Law 25 requires disclosure when a decision about a person is made exclusively by automated processing, which is a design constraint on any agent that gates who gets contacted or approved.

It can draft them; it should not file them. Alberta's measurement and reporting regime is built on traceability, and the relevant instruments are specific. Directive 017, Measurement Requirements for Oil and Gas Operations, sets the measurement, accuracy and proving requirements. Directive 076, Operator Declaration Regarding Measurement and Reporting Requirements, is the operator's own declaration of compliance with that regime. Volumetric, valuation and allocation data is reported through Petrinex, the shared petroleum information system used by Alberta, Saskatchewan and British Columbia, and those submissions have to reconcile to what the ledger and the production accounting system say. So we build agents that assemble a draft and show their work: every figure in the draft carries a link back to the source record, the retrieval passage or the calculation that produced it, and any figure the agent could not source is flagged rather than filled in. Submission itself stays a human action behind an approval gate, the agent has no write access to Petrinex or to the production accounting system, and the action log retains the prompt, the tool calls, the output and the approver so a joint venture auditor or a regulator can follow the chain afterwards.

It can do the reconciliation work that currently eats analyst time, and it can do it without deciding anything. Directive 060, Upstream Petroleum Industry Flaring, Incinerating, and Venting, sets Alberta's flaring, incinerating and venting requirements, and federally the Greenhouse Gas Reporting Program requires annual emissions reporting above the 10,000 tonne carbon dioxide equivalent threshold, alongside carbon pricing obligations under the federal output-based pricing system and Alberta's TIER regulation. The practical problem is that measurement data arrives from several sources in different formats and on different schedules, continuous monitors, aerial and drone surveys, handheld inspections and engineering estimates, and reconciling them into one defensible number is slow, repetitive and well suited to an agent. So we build the agent to normalise and reconcile those inputs, surface where a bottom-up estimate and a measured value disagree and by how much, and assemble a draft report with each figure traced to its source. Judgement calls about which value to report, and the filing itself, stay with the people accountable for it. The reconciliation trail is retained for whoever verifies the numbers afterwards.

By putting it on the other side of a boundary that was designed before the agent was. Agents live on the information technology side of an IEC 62443 zone and conduit architecture. They read from the historian, from an integration layer or from an API the operations team already exposes, and they do not connect to controllers, remote terminal units or the control network. There is no configuration flag that would let them; the network path does not exist. Where a workflow genuinely needs a change in an operational system, the agent prepares the request and a person executes it through the existing change management process, which keeps the operator's own controls intact rather than routing around them. We build to NIST SP 800-82 and Canadian Centre for Cyber Security guidance for the industrial layer, isolate the agent runtime with its own identity and least-privilege credentials, hold secrets in a managed vault in the contracted Canadian region, and treat every retrieved document and every tool response as untrusted input, because prompt injection through a maintenance note or a supplier PDF is a realistic attack rather than a theoretical one. Every agent ships with a kill switch your duty manager can use without calling us.

A single-workflow production agent typically takes fourteen to twenty-four weeks from kickoff, and the rollout matters as much as the build. Weeks one to four are workflow observation, tool and data inventory, the governance perimeter, and an autonomy definition that names which actions need approval. Weeks five to twelve are build: integrations, retrieval, guardrails, the action log, and an evaluation set assembled from your own historical cases rather than a public benchmark. Weeks thirteen to eighteen the agent runs in shadow mode beside the people doing the work, producing output nobody acts on, which is the only cheap way to find out where it is wrong. Then it goes live on the narrowest useful slice with approval required on every action, and autonomy widens only where the evaluation numbers justify it. We instrument disagreement rate, not just accuracy, because the signal that matters early is how often a reviewer overrides the agent and why. Teams lose faith in an agent that was given too much scope too early far faster than they lose patience with one that starts small, so we start small deliberately.