AI Agent Development Company in Toronto

Bands, and they move with governance far more than with model choice. A scoped pilot on one workflow, with a small tool set, a real evaluation set and a written scope of authority, typically runs CAD 45,000 to 110,000 over six to ten weeks, and it earns its cost mainly by producing the evidence needed to kill the idea cheaply when the task turns out to suit a script better than an agent. A production agent inside a mid-market Toronto organization, integrated with three or four systems, with tool-layer controls, audit logging, injection testing and monitoring the client's team can operate, typically runs CAD 150,000 to 400,000. A regulated deployment inside a bank, insurer or provincial agency, where model risk documentation, shadow running, internal audit review and a formal approval path are part of scope, typically runs CAD 400,000 to 1,200,000, and there the review calendar rather than the engineering sets the schedule. Inference, tool execution and reranking are ongoing costs quoted separately from build, priced per resolved task rather than per token, because token estimates hide how many times an agent loops before it finishes. We do not price on seat counts or on a promised deflection rate, because neither is knowable before the evaluation set exists.

No, and a proposal that says otherwise is worth reading closely. The Artificial Intelligence and Data Act was Part 3 of Bill C-27, tabled in June 2022. It never completed committee study, and it died on the Order Paper when Parliament was prorogued on January 6 2025. It is not in force. There is no AIDA high-impact classification with legal effect, no AIDA audit, and no certificate anyone can issue you. The obligations that do apply are narrower and more specific, and they are the ones we scope against: Quebec's Law 25 rights where a decision is based exclusively on automated processing, the Ontario job posting disclosure that begins January 1 2026 for employers with twenty-five or more employees, Ontario's Responsible Use of Artificial Intelligence Directive for ministries and provincial agencies, the Treasury Board Directive on Automated Decision-Making and its Algorithmic Impact Assessment for federal institutions, and PIPEDA and PHIPA over the data the agent consumes and produces. We do apply a risk tiering when designing an agent, borrowed from published international vocabularies, but we label it as our own engineering practice rather than as law, because dressing an internal framework as a statutory duty is how buyers end up funding the wrong controls.

The tool layer, never the prompt. A prompt is a request and a permission is code, and the difference shows up the first time a model is talked into something. Concretely: every tool the agent can reach is on an explicit allow-list, and anything absent is unreachable rather than discouraged. Each tool holds its own least-privilege credential from a secrets manager, so a retrieval tool physically cannot post a transaction. Limits that matter are enforced server side in the tool implementation, meaning a per-call cap, a per-session cap and a daily cap on money, records touched and messages sent, checked against the caller rather than trusted from the model's arguments. State-changing calls carry idempotency keys so a retried or duplicated plan cannot post twice. Anything externally visible sits behind a confirmation step with the proposed action rendered in full for the human approving it. Every plan, tool call, argument set, observation and outcome is written to an append-only log with the request that started it. And there is a documented stop procedure that disables the agent without a deployment, tested before launch rather than discovered during an incident.

By assuming injection succeeds and making the consequence small. An agent that reads a customer email, a supplier PDF, a web page or a ticket comment is reading text an attacker can write, and no amount of instruction hardening reliably prevents a model from being persuaded. So the defences are structural. Retrieved and inbound content is fenced and labelled as data rather than instruction, and the system prompt is not the only thing standing between that content and a tool. Tool authority is scoped so the worst case of a successful injection is bounded by the caps and the allow-list rather than by the model's judgment. State-changing actions stay behind human confirmation for exactly this reason, since the confirmation step is what an injected instruction cannot satisfy on its own. Outputs are validated against a schema before anything downstream consumes them. Retrieval sources are allow-listed, and content from open web fetches is treated as the least trusted tier. Before launch we red-team the agent with direct instructions, injected content inside documents it will genuinely retrieve, and multi-hop payloads that try to reach a tool through a second agent, then deliver a written report with severities, fixes and a retest.

The evaluation set is built before the agent, not after it, and the client owns it. It holds real tasks drawn from the actual queue with known-good outcomes, edge cases the team already argues about, adversarial inputs, and out-of-policy prompts where refusal is the correct result. What we measure is task success on those cases, not model benchmark scores, since a published benchmark says nothing about whether an agent can close a specific claim file correctly. Alongside success rate we track the rate of unsupported statements against grounded sources, escalation rate, tool error rate, steps per completed task, latency at the median and the ninety-fifth percentile, and cost per resolved task in Canadian dollars. That set runs in continuous integration on every prompt, tool or model change, and a regression blocks promotion rather than raising a warning nobody reads. In production, traces flow to Langfuse or LangSmith or into the client's existing observability estate, with sampled human review on a fixed cadence, drift and escalation trends reported to whoever owns the process, and a re-evaluation whenever an upstream model version changes underneath you, which happens more often than vendors advertise.

Twelve to twenty weeks is typical for a first production agent, and the spread is set by review load rather than by engineering. Weeks one to three are the authority workshop, the scope of authority document, the regulatory scoping and the tool inventory, which is where most of the argument happens because teams discover they disagree about what the agent is for. Weeks four to eight build the first end-to-end loop with real retrieval and two or three integrated systems, alongside the evaluation set. Weeks nine to fourteen are the unglamorous majority of the work: tool-layer caps and credentials, confirmation gates, audit logging, injection red-teaming, failure and retry behaviour, and the operator tooling a support team needs when an agent stalls halfway through a plan. From there the agent runs in shadow mode against live traffic while its output is compared with what humans did, which is the only honest test of whether it is ready. Rollout is staged by queue or by customer segment with a stop threshold agreed in advance. Regulated deployments inside a bank, insurer or provincial agency commonly add six to twelve weeks of internal audit and approval on top, and that time is not compressible by adding engineers.