AI agent deployment Canada: production agentic systems

The two products solve different problems and they pair cleanly. Open Claw Pro is the stack: the Canadian-region cloud infrastructure, the observability tooling, the audit trail, the 99.5% SLA, and the multi-tenant inference path. Open Claw Pro is the thing you run AI on. An AI agent deployment Canada engagement, by contrast, ships the agents that run on the stack. The agent is the production-grade workflow automation: lead qualification, internal search, customer support, document review, code review. Pick a stack tier first: Open Claw Pro for managed Canadian cloud, Open Claw Enterprise for sovereign-tier cloud, or Sovereign AI Box for hardware-on-prem. Then ship one or more Agent Deployment engagements on top of the chosen stack. Operators without a stack can pick one as part of the Agent Deployment engagement; we provision the stack alongside the agent build.

The operator picks on the kickoff call and the agent runtime supports both paths. The default options cover four families. First, the operator's existing model contract: Claude on Anthropic, GPT-4 on OpenAI, Gemini on Google, or a Bedrock or Vertex contract. Second, a self-hosted open-weight model running on the operator stack: Llama 3.1 or Qwen 2.5 on Open Claw Pro, Enterprise, or Sovereign AI Box. Third, a hybrid where the agent routes some prompts to a high-quality SaaS model and others to a self-hosted model based on cost or data-residency. Fourth, a model swap mid-engagement if the eval suite shows a better fit elsewhere. The agent specification documents the model choice and the runtime supports a swap without changing the agent code.

One production agent per deployment at the $5,000 CAD baseline. One agent means one workflow: one lead-qualification agent, OR one internal-search agent, OR one customer-support agent, OR one document-review agent, OR one code-review agent. Multi-agent deployments scale linearly on the engagement call: two agents at twice the baseline, three at three times, and so on. The eval suite and the observability dashboard scope to the agent count. The operator runbook documents each agent separately. For organisations shipping more than five agents we recommend a standing engagement instead of repeat one-off deployments. The standing engagement amortises the eval suite and dashboard work across the agent fleet.

We build the evaluation suite as part of every AI agent deployment Canada engagement. Twenty to fifty gold-set input/output pairs at launch. The operator validates the gold set on the kickoff and the scoping calls. The operator owns the ground truth and we structure it into the suite. The suite scores four dimensions: accuracy against the gold set, hallucination rate measured by retrieval-grounded checks, tool-call correctness against the tool-permission scope, and runtime cost per call. The suite reruns on every agent change after launch and the runs land in the observability dashboard. This catches regressions before they reach the operator workflow. The operator runbook documents how to add new gold-set pairs after handover so the suite stays current with the agent's production behaviour.

The five example buckets are the use-cases we ship most often, not a closed list. Those buckets cover lead qualification, internal search, customer support, document review, and code review. The kickoff scoping call confirms fit for use-cases outside the buckets. Common adjacent use-cases include sales research agents, RFP-response drafting agents, security alert triage agents, finance reconciliation agents, and HR onboarding agents. Most operator workflows that involve reading a structured source, applying domain reasoning, and producing a structured output fit cleanly into the Agent Deployment shape. The baseline price holds for custom use-cases that fit the four-to-eight-week timeline. Use-cases requiring bespoke LLM training, custom tool development beyond the default palette, or hardware procurement sit outside the engagement and route to a separate scope.

We hand over the operator runbook at day thirty with the agent stable in production. The operator owns the agent from day thirty onward. The handover covers four things. First, the runbook documents daily operations (the dashboard check), weekly tuning cadence (eval rerun + gold-set expansion), monthly eval refresh (gold-set rotation), and the escalation path back to vanwebdev for harder issues. Second, the eval suite stays wired so the operator catches regressions as the agent runs. Third, the community Slack access continues for the operator and one named technical stakeholder. Fourth, operators wanting longer-term tuning support engage a separate retainer or book a follow-on Agent Deployment for the next workflow. Most operators ship the second agent within ninety days of the first handover.