The Integrations Holding Your AI Agent Together Matter More Than the Agent Itself

The fitness industry has had a technology integration problem for as long as I can remember. Club management systems and CRMs treated their APIs as an afterthought for years. Sparse documentation, gated access, awkward data models. If you wanted two platforms to communicate properly, you were in for a battle. This was the single biggest constraint on digitally delivering value to operators. It stalled everything from routine member messaging to anything resembling intelligent automation.

That constraint is now mostly gone. The major vendors have opened up. APIs are available, documented, and capable of real-time, two-way data exchange. That maturity, paired with the leap in foundational AI models, is what led us to begin building Antares in 2024. From day one, our second largest team after our LLM engineers has been platform and tools integration. That was deliberate. We understood immediately that the strength of an AI agent is ultimately shaped by the depth and reliability of its connections to the systems that actually run a fitness business. The agent is the brain. Integrations are the nervous system. Either one without the other is worthless.

This is the side of building an AI agent that rarely gets airtime at conferences or in glossy demos: the integration layer. The connective tissue between the agent and your operational reality. It is also what separates a real platform from a stitched-together prototype. For operators, the difference comes down to how that connection is constructed.

The Difference Between a Direct Line and a Postal Service

There are broadly two ways to connect an AI agent to the systems it must work with. The first is direct API integration, where the agent and the target platform communicate in real time, bidirectionally, with full context. The second is via middleware services like Zapier, which sit in the middle and pass data between apps using simple trigger and action logic. If something happens in Application A, then do something in Application B.

For small businesses connecting a few tools, middleware is good enough. A form submission adds a row to a spreadsheet. An email arrives and a task is created. Millions of businesses use Zapier for exactly this, and it works fine for that use case.

An AI agent operating inside a fitness business is a fundamentally different ask. The agent must read system data, interpret it in context, decide what to do, write updates back, and do it all within the timing of a natural conversation with a member or prospect. That can mean checking billing status, pulling attendance history, updating a CRM record, and sending a personalised message, all inside one interaction.

Middleware cannot do this. It is trigger-based. Something happens, then something else happens. There is latency between steps. There is no concept of an ongoing conversation. There is no ability to pull from three systems, synthesise the context, and act inside a single exchange. The agent asks, waits for a relay, receives a partial response, sends another relay, waits again.

Think of it like this. Zapier is a postal service. It collects a parcel from one place and delivers it somewhere else. A direct API integration is a phone line: two systems talking in real time, both ways, with full context. When you need fast, multi-system coordination, the postal service is not enough.

The Compounding Problem at Scale

Reliability is where this gap becomes genuinely dangerous. Middleware has rate limits. It has multiple failure points. When a workflow breaks, and they do break, it often fails quietly. No alert. No one notices until a member slips through the cracks, a lead goes cold, or a billing issue sits unresolved for weeks.

At one site, you might spot it. Across 5, 10, 50, or 100 sites, you will not. Failures stack up and the damage happens before you even know something is wrong.

Data governance adds another risk. Every time information moves through a middleware relay, member data, billing details, attendance behaviour, personal contact information, all passes through a third-party intermediary that has no reason to be in the chain. Direct API integration keeps data where it should be, moving between your systems and your agent without unnecessary exposure.

Then there is customisation. Multi-site operators do not run a single uniform business. Sites vary. Pricing differs by region. CRM setups differ by brand or acquisition history. Booking platforms may not even be consistent across the estate. Trying to manage that through middleware workflows, where each variant needs its own set of triggers and actions, is not workable. It becomes a maintenance nightmare that worsens every time you add a site.

If you do not have the engineering depth or domain expertise to build and maintain direct integrations with the systems fitness operators actually use, middleware is a practical shortcut. It gets something running. It resembles integration if you do not examine it too closely. But operators should be clear about what they are purchasing. It is a workaround, not architecture. And workarounds expire.

MCP: The Next Evolution in How Agents Connect

There is another shift happening right now that most of the fitness industry has not encountered yet. It is called the Model Context Protocol, or MCP.

Traditional API integrations are like direct phone lines between two specific systems. You build the connection, you maintain it, and it works. MCP is closer to a universal translator: a standard protocol that lets an AI agent discover and use available tools dynamically, without requiring a bespoke integration for each one.

Antares already uses MCP across many platform interactions, including CRM workflows with HubSpot and our proprietary Veritas system. In practice, this means the agent can access those platforms natively: reading, writing, searching, updating, through a standardised interface rather than limited wrappers built from scratch.

As more vendors expose MCP servers, and they will, a properly built agent platform gains new capabilities without repeatedly rebuilding integrations. The platform appreciates over time. It becomes more powerful with every new tool made available through the protocol. A solution dependent on middleware cannot even start that journey. It is locked into a relay model while the industry moves toward native, dynamic tool use.

The Reality for Multi-Site Operators

Whether you run 5, 10, 50, or 100 sites, the bar for any AI solution is simple. It must genuinely operate inside your business, with all its complexity, across every location, consistently and reliably.

That demands deep, direct integration with your club management system, your CRM, your booking and scheduling stack, and your communications platforms. It demands mechanisms like retrieval-augmented generation and contextual knowledge management so the agent can access site-specific truth: pricing, timetables, policies, promotions, all kept current and accurate. And it demands an orchestration layer that can handle multi-site, multi-brand, multi-system operations without collapsing.

Trying to stitch that together with middleware workflows is like attempting to run a Formula 1 team using duct tape and optimism. It might survive a lap. It will not survive a season.

The Question That Tells You Everything

When you assess any AI solution for your operation, there is one question to ask before anything else: show me how you connect to my systems. Not a diagram. Not a slide. Show me the integration. Show me how the agent reads and writes data in real time. Show me how it handles site-specific configurations. Show me what happens when something breaks.

The answer tells you whether you are buying a platform designed for the long run or a prototype held together with shortcuts.