AI agent deployment Canada productises bespoke agentic systems work. The Agent Deployment engagement builds one production agent for one operator workflow. We ship the agent to production on Canadian infrastructure. We wire the observability dashboard. We run the evaluation suite. We hand over the operator runbook. The default cadence runs four to eight weeks from kickoff to operational, with a 30-day post-launch tuning window built into every engagement.
Most operators reach for an off-the-shelf agent builder. However, those tools ship a generic agent that does not match the operator workflow. Additionally, they route prompts and tool calls through a US data plane. In contrast, an AI agent deployment Canada engagement starts with your workflow on paper. First, we map the workflow into an agent specification written in plain English and structured JSON. Next, we pick three to six tools the agent needs. Then, we wire the retrieval index if the agent reads operator documents. Finally, we ship it to production on Canadian infrastructure under your operator control.
The example agent use-cases we ship most often fit five buckets. First, lead qualification agents that read an inbound form and route the lead. Second, internal search agents that read a Notion or SharePoint corpus and answer staff questions. Third, customer support agents that read a knowledge base and draft first-pass replies. Fourth, document review agents that read a contract or policy and flag clauses. Fifth, code review agents that read a pull request and post structured review notes. Notably, every bucket is bespoke to the operator workflow. Furthermore, every bucket ships with the eval suite plus observability plus runbook.
Agent Deployment differs from Open Claw Pro in one clean line. Open Claw Pro is the stack: the infrastructure, the observability, the audit trail, the 99.5% SLA. Agent Deployment is the agents that run on the stack. Likewise, Open Claw Enterprise and Sovereign AI Box are stack options. Every AI agent deployment Canada engagement lands on one of three stack choices. Specifically, the operator picks Open Claw Pro for managed Canadian cloud, Open Claw Enterprise for sovereign-tier cloud, or Sovereign AI Box for hardware-on-prem. Otherwise, we provision the stack as part of the engagement.
The agent specification is the spine of every AI agent deployment Canada engagement. We write it in plain English first so the operator can read it. Then we structure it as JSON so the agent runtime can read it. The spec covers the agent role, the goal, the input shape, the output shape, the tool list, the retrieval scope, the safety guardrails, and the escalation path. The plain-English version sits in the runbook. The JSON version sits in the agent runtime configuration.
Tool integration runs three to six tools per agent. The default tool palette covers a browser for read-only web research. It also covers a SQL or API client for operator-system reads. It includes a document retriever for the retrieval index. It adds a vector search tool for semantic queries. It rounds out with a write-back tool when the agent needs to update an operator system. We scope the tool list on the kickoff call. The agent runtime uses structured tool calls based on the Model Context Protocol pattern documented in the Anthropic tool-use guidance. Tool permissions land in the AI agent deployment Canada specification and lock down at deploy time.
The evaluation suite runs against the agent on every AI agent deployment Canada engagement. We start with a small gold set of 20 to 50 input/output pairs the operator validates. The agent runs against the gold set. The eval scores accuracy, hallucination rate, tool-call correctness, and runtime cost per call. The eval suite reruns on every agent change after launch. This catches regressions before they reach the operator workflow. We cover the agent-specific risks documented in the OWASP LLM Top 10. The risks include prompt injection, insecure output handling, training data poisoning, model denial of service, and overreliance.
Observability ships as a dashboard the operator owns. Every agent run emits a structured event. The event captures the input, the tool calls, the model output, the runtime cost, the latency, and the eval score. The dashboard reads the event stream. It renders the run history. The operator reads the dashboard daily for the first 30 days. Regressions surface early. The dashboard exposes a kill switch the operator triggers if the agent misbehaves. The kill switch parks the agent in a read-only mode. It pages the operator on-call rotation.
The 30-day post-launch tuning window catches the gap between specification and reality. First, we read the production event stream for the first 10 days. We flag agent runs that deviate from the eval baseline. Next, we propose tuning changes the operator approves. Then, we ship the tuning changes through the eval suite. Finally, we hand over the runbook at day 30 with the agent stable in production. The runbook covers daily operations. It covers weekly tuning checks. It covers monthly eval refreshes. It documents the escalation path to vanwebdev for harder issues.
Agent Deployment sits inside the Build trunk. The deeper foundation comes from our agentic systems pillar. Agent Deployment is the productised version of that work. Buyers who need a foundation pass before committing to an agent book our Sovereign Infrastructure Brief. Buyers who need an AI footprint mapped first book our Intelligence Audit. Buyers who need a website to host the agent surface book our Hermes website build. Patterns and runbooks sit in the Library. Lab work and benchmarks sit at Research.
AI agent deployment Canada pricing runs $5,000 CAD per agent at the engagement baseline. The price covers the agent spec. It covers the tool integration. It covers the retrieval index if needed. It covers the production deployment. It covers the observability dashboard. It covers the eval suite. It covers the 30-day post-launch tuning. It covers the operator runbook. Stack provisioning costs sit separately. Open Claw Pro, Open Claw Enterprise, or Sovereign AI Box options price as the buyer picks the stack tier. Multi-agent deployments scale linearly on the call. Timeline runs four to eight weeks. The narrow end suits agents with a clean spec and an existing stack. The wide end suits agents that need a fresh retrieval index plus a new stack provisioned alongside.