Build an AI agent yourself or hire a custom one?

What you can build yourself today

The main AI assistants let you set up custom versions, with fixed instructions, reference files and, in some cases, connections to other tools:

• ✓GPTs, in ChatGPT: assistants with instructions, files and actions. On business plans (Business, Enterprise and Edu), creating and publishing them depends on the permissions set by the admin.
• ✓Gems, in Gemini: assistants with their own instructions, inside Google's ecosystem.
• ✓Projects, in Claude: instructions and reference files organized by project, shareable with the team on team plans.
• ✓Agents in Microsoft 365 Copilot and Google Workspace Studio: assistants tied to each platform's documents and work tools.

Where do-it-yourself works well

For internal productivity, these assistants solve a lot: reviewing text to the company's standard, answering questions from a manual, drafting proposals from templates, summarizing meetings. They cost only the subscription the company already pays and are ready in a few hours.

They are also the best way to validate an idea. If a GPT or a Gem handles a task well for three people, you've learned what works before investing in something bigger.

Where it stops

The limits show up when the agent needs to leave the chat window:

• ✓Channel: these assistants live inside the vendor's app. To serve customers on WhatsApp or your website, you need integration with that channel's platform.
• ✓Systems: reading and updating a CRM, ERP or calendar securely requires integrations and permissions that do-it-yourself doesn't cover.
• ✓Proactivity: a custom agent can act on its own from an event, such as a new lead or a late payment. A chat assistant waits for someone to open the conversation.
• ✓Volume and cost: customer service at scale needs per-conversation cost control and queues.
• ✓Data and auditing: a log of every action, per-person permissions and data protection compliance.
• ✓Maintenance: metrics, tests and continuous adjustments, because agents get worse over time if nobody looks after them.

Comparing the three paths

Between do-it-yourself and custom there's a middle path: no-code automation platforms.

Five signs it's time for a custom agent

If two or more of these show up, do-it-yourself is no longer enough:

• ✓The agent will talk to customers, not just to the team.
• ✓It needs to read and write in company systems, such as the CRM, calendar or ERP.
• ✓Volume goes beyond a few dozen conversations a day.
• ✓Personal or confidential data is involved.
• ✓A mistake by the agent costs money or reputation.

What makes up the cost of each path

With do-it-yourself, the cost is each person's subscription. With a no-code platform, there's the platform plan, setup hours and AI model usage through the API. With a custom agent, there's development, infrastructure, model usage and agent support after delivery.

Comparing only the upfront price is misleading: a custom agent that serves a thousand customers a month can cost less per conversation than hours of manual work, or than a poorly configured assistant that creates rework.

Checklist before deciding

Before choosing the path, answer:

• ✓Who will talk to the agent: the team or customers?
• ✓Which channels does it need to be on?
• ✓Which systems does it need to read and update?
• ✓How many conversations or tasks per month?
• ✓What data will it handle and who can see what?
• ✓Who looks after it once it's live?

How SINOON helps

Often the best start is a simple assistant to validate the idea. When it proves itself, we build it as a custom AI agent, connected to the company's channels and systems, and look after it afterwards through AI agent support. If you're still unsure what an agent is, see the difference between a chatbot and an AI agent.