Custom AI Agent Systems: Claude, GPT, Gemini, and MCP Integrations

What are Full Percent's AI agent systems?

They are custom AI agents built for a specific business job, routed across Claude, GPT, and Gemini, and connected to the tools the business already uses through Model Context Protocol (MCP) integrations such as Stripe, Cloudflare, Gmail, Google Drive, and QuickBooks. They are quoted per project.

MCP is an open protocol for connecting AI applications to external tools and data sources in a standard way (modelcontextprotocol.io, 2026). Instead of one-off glue code for every model and every app, an agent reaches each system through an MCP server, which makes it practical to switch or combine models without rebuilding the integrations.

What's included

Every build is custom, but the components are consistent.

• Agent design. Defining the job, the inputs, the decisions the agent may make on its own, and the ones it may not.
• Multi-model routing. Sending each task to Claude, GPT, or Gemini based on the task, rather than locking the system to one vendor.
• MCP integrations. Connecting the agent to business systems such as Stripe, Cloudflare, Gmail, Google Drive, and QuickBooks.
• Omnichannel delivery. Putting the agent where people already talk: SMS, email, Telegram, Slack, and WhatsApp.
• Human-escalation design. Rules for when the agent hands off to a person, who gets notified, and what context travels with the handoff.
• Messaging compliance. When agents send SMS or email, the same consent, opt-out, and registration requirements apply; see SMS and 10DLC compliance and email deliverability.

Which channels and integrations are supported?

The channels are SMS, email, Telegram, Slack, and WhatsApp; the named MCP integrations are Stripe, Cloudflare, Gmail, Google Drive, and QuickBooks. Omnichannel campaign setup takes about 2 days.

Other systems can be connected where an MCP server or API exists; that is part of scoping.

How a build works

A build moves from one narrow job to a working agent, then widens only after the first job is reliable.

1. Scope one job. Pick a task with a clear input, a clear output, and a way to tell if it went right, such as answering inbound lead texts or triaging a support inbox.
2. Map systems and permissions. List which tools the agent needs and what it may read versus change. Write access is granted narrowly.
3. Design escalation. Decide the conditions that route to a human: low confidence, money movement, complaints, legal or compliance questions, or a customer asking for a person.
4. Build and connect. Wire models, MCP integrations, and channels.
5. Test on real cases. Run the agent against real examples before it talks to customers, and review its decisions.
6. Launch and watch. Go live on one channel, review transcripts, then expand.

Why human escalation is designed first

An agent is only as safe as its handoff rules. Designing escalation up front decides what the agent never does alone.

• Actions that move money or change records can require human approval.
• Conversations that turn angry, legal, or sensitive route to a named person with the full transcript.
• Opt-out requests on SMS and email are honored by the system, not left to model judgment, because TCPA and carrier rules require it.

What drives the quote

AI agent systems are quoted per project. The main factors:

• Number of jobs the agent performs and how many decisions it makes without a person.
• Number of integrations, and whether each needs read or write access.
• Number of channels, and whether SMS or email sending requires registration or deliverability work first.
• Escalation complexity: who gets notified, through what channel, and during what hours.
• Whether you need ongoing monitoring and tuning after launch.

What's not included

We build and connect the system; some costs and decisions stay with you.

• Model and platform usage fees. Charges from model providers, messaging platforms, and SaaS tools are billed by those providers.
• Fully unsupervised operation for high-risk actions. Escalation to a human is part of the design, not an optional extra.
• Outcome guarantees. We do not promise conversion rates or revenue figures.

Who this is for

Businesses with repetitive, text-heavy work spread across several tools.

• Lead-generation businesses in funding, insurance, and debt guidance that respond to inbound SMS and email.
• Operators who live in Slack, Gmail, and QuickBooks and want an agent that can look things up across them.
• Teams that want to use more than one AI model without rebuilding integrations each time.

Full Percent's founder, Stephen Ventura, built two-way LLM SMS funnels before ChatGPT launched. Contact Full Percent to scope a build.

Stephen Ventura founded Full Percent in Boca Raton, Florida. He has built email systems since 1997 and SMS platforms since before the iPhone. stephenventura.com