AI Agents & Automation

An AI agent is a software system that takes a goal, plans a sequence of steps to reach it, executes those steps using tools (APIs, databases, browsers, code), and adapts when steps fail. A chatbot answers individual messages; an agent completes multi-step tasks autonomously. Modern agents are typically powered by large language models operating through tool-use loops.

Use a multi-agent system when one workflow has distinct roles that benefit from separation, for example one agent that gathers information, one that checks it and one that writes the output. When the work fits one coherent role, a single agent is faster, cheaper and easier to debug, so that is where we start.

We ground agents in three ways: retrieval from your approved documents, a citation to the source document for every factual claim, and tool boundaries that limit what the agent can return. Outputs below a confidence threshold go to a person. Wrong answers come from different causes, such as missing retrieval, outdated sources or vague instructions, and each gets its own fix.

Remolda's six-week AI Pilot Sprint puts one agent into production for $9,800 CAD + HST, including integration, guardrails, training and a runbook. Larger multi-agent systems are estimated after the pilot. Model usage is billed by the provider and depends on volume.

Model Context Protocol (MCP) is an open standard for connecting AI assistants to data sources and tools through a JSON-RPC interface. We recommend MCP servers when you have multiple AI clients (Claude Code, Claude Desktop, Cursor, internal apps) that need access to the same data — building one MCP server is cheaper than building per-client integrations. For single-client deployments, plain function-calling is usually simpler.

Sensitive data is handled with layered controls: retrieval scoped to what the requesting user may read, removal of identifiers before model calls where the task allows it, logging of every model call, and enterprise or API plans whose terms exclude training on your data by default. Processing location is documented per vendor: for example, Claude on Amazon Bedrock keeps data at rest in Canada while inference runs in US or global regions, and Azure OpenAI offers in-geography processing in Canada East for some models.