AI Agent Development: Custom and Multi-Agent Systems
AI agent development is building software that plans a task, uses your tools and data through APIs, and hands off to a person when unsure. Remolda builds a first production agent in six weeks for $9,800 CAD + HST.
In short
- An agent is a language model with a goal, a set of tools (APIs, databases, documents) and rules on what it may do alone and what needs approval.
- Start with one agent on one job. Multi-agent systems make sense when a process has distinct roles, such as intake, research, drafting and checking.
- Built on Claude, OpenAI GPT models or Azure OpenAI, orchestrated in code or n8n, with every action logged.
- Price: $9,800 CAD + HST for a six-week AI Pilot Sprint that puts one agent into daily use.
- Spending money, deleting records and messaging clients stay behind human approval until measured results justify more autonomy.
Your situation
Agents are worth building when a task needs judgment across several tools, and a fixed script keeps breaking on exceptions. Typical cases:
- Research and preparation. Before a meeting, a claim or an application review, someone pulls data from four systems and writes a summary.
- Case handling. Each request needs lookup, a decision within policy, a drafted response and an update in the system of record.
- Operations follow-up. Overdue items, missing documents, unanswered emails: someone chases them every morning.
- LLM features inside your software. You deliver software to clients and want an agent inside it that uses your data safely.
Single agent or multi-agent?
| Situation | Recommended design |
|---|---|
| One task, a few tools, clear rules | One agent with tools and approval steps |
| Several roles with different permissions (intake, drafting, checking) | Multi-agent: specialized agents plus an orchestrator |
| Fixed sequence, AI needed only for reading or writing | Workflow automation with an AI step |
| Mostly documents in, fields out | Intelligent document processing |
We start with the simplest design that does the job. A second or third agent is added when the first one's logs show where it struggles.
What the AI Pilot Sprint includes
One agent in daily use. Defined goal, tool list, permissions and approval rules, running on real cases.
Tool connections. Microsoft 365, Google Workspace, CRM, ticketing, databases or your own APIs. Older systems are covered in LLM integration with legacy systems.
Evaluation set. Test cases from your real work, run before launch and before every change.
Audit trail. Prompts, tool calls, outputs and approvals, stored where your team can review them.
Runbook and training. The owners learn to read logs, adjust instructions and handle escalations.
How long it takes
Six weeks for the first agent. In our experience the build itself is rarely the slow part; tool access, test data and agreement on approval rules set the pace. Multi-agent systems usually follow after the first agent has run for several weeks.
What it costs
The AI Pilot Sprint is $9,800 CAD + HST for one agent in production. Model usage is paid by you to the provider. Multi-agent systems and agents embedded in your own product are priced after the pilot, on a call.
Choosing between several processes? The two-week AI Opportunity Audit ($2,900) ranks the options. Full list on the pricing page.
Why Remolda for AI agent development
- Simplest design first. An agent only where a fixed workflow would break.
- Measured before launch. Evaluation sets built from your cases.
- Model-neutral. Claude, GPT models or Azure OpenAI, chosen by test results and data rules.
- Governance built in. Permissions, approvals and logs align with your AI use policy.
- You own it. Code, prompts and logs in your tenant; contract with a Canadian company.
How the work runs
The Sprint is the Implement step of the Remolda Cycle (Audit → Strategy → Implement → Empower → Evolve).
- Scope. Goal, tools, permissions, approval rules, success measure.
- Evaluation set. Real cases with expected results.
- Build and test. The agent runs against the set until results meet the agreed bar.
- Live use. Approvals on; logs reviewed with your team weekly.
- Review. Go, adjust or stop; next agents move to the Evolve step.
Frequently asked questions
What is an AI agent?
An AI agent is a program built around a language model that receives a goal, decides on steps, calls tools such as search, databases, email or your CRM, checks the results and either finishes the task or asks a person for help.
What is a multi-agent system?
A multi-agent system splits one process among several specialized agents, for example one that gathers information, one that drafts and one that checks against rules, coordinated by an orchestrator. It helps when the roles need different instructions, tools or permissions.
How much does it cost to build a custom AI agent?
Remolda's six-week AI Pilot Sprint puts one agent into production for $9,800 CAD + HST. Larger multi-agent systems are scoped after the pilot. Model usage is billed by the provider and depends on volume.
How are enterprises adopting multi-agent AI in 2026?
We recommend adopting agents step by step: one agent on a narrow, high-volume task, human approval on consequential actions, logs reviewed weekly, then more agents or more autonomy once error rates are measured. Governance and access control usually decide the pace.
Which model do you build agents on?
Claude, OpenAI GPT models via the OpenAI API, and Azure OpenAI for organizations inside Microsoft Azure. The choice depends on task quality in testing, data residency needs and your existing contracts.
How do you keep an agent from doing something wrong?
Least-privilege access to tools, explicit rules on which actions need approval, confidence thresholds, test cases run before every change, and a full log of prompts, tool calls and outputs that your team can review.
What is the difference between an AI agent and workflow automation?
Workflow automation follows a fixed sequence with an AI step inside it. An agent chooses its own steps within limits. Where a fixed sequence works, we build the simpler automation first.
Who owns the agent and the code?
You do. Code, prompts, configuration and logs live in your repository and cloud tenant, and model accounts are in your name.
Sources
- Anthropic — Claude for Enterprise
- OpenAI — Business data privacy, security and compliance
- Microsoft Learn — Foundry models sold by Azure: region availability
Facts checked:
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