The Core Components of Real AI System Design
Forget the hype. Most of what people call “AI agents” are just fragile scripts. Real ai system design isn’t about finding the magic prompt; it’s about building a durable process that can handle failure. I’ve built dozens of these systems for my businesses, from lead scrapers to automated blog engines, and they all share the same three-part skeleton.
You need three things:
- An Orchestrator: This is the brain. It’s the tool that executes steps in a sequence, passes data between them, and handles the logic. This is not a Python script you run locally. It’s a cloud-based tool like the Make platform or Zapier automations. It listens for a trigger (like a new row in a spreadsheet) and kicks off your workflow.
- A State Machine / Data Store: This is your system’s memory. It’s where you track the status of every single job. A simple Google Sheet or, preferably, an Airtable base is perfect. Without this, you have no idea what’s running, what’s finished, or what’s failed.
- The Action Layer: These are the tools that do the work. The most common one is an LLM API call to OpenAI or Anthropic. But it also includes scraping tools, email senders, data enrichment services, and anything else your system needs to interact with.
That’s it. Orchestrator, Data Store, Action Layer. Everything else is a detail. If you’re missing one of these, you don’t have a system. You have a toy.
What Most Guides Get Wrong: State Management
Almost every tutorial I see online shows a simple, linear flow. Trigger -> LLM -> Output. They show a perfect run where everything works. This is a lie.
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In production, things break. APIs go down. LLMs return garbage JSON. Your scraper gets blocked. A linear, stateless workflow has no way to recover from this. If step 2 of 3 fails, the whole run is lost. You can’t easily retry it, and worse, you might not even know it failed unless you’re watching the logs like a hawk.
This is why a dedicated state machine is the most critical, and most overlooked, part of any ai automation blueprint. Your Airtable base isn’t just a place to dump inputs; it’s a living dashboard for your system. Here’s a minimal setup I use for almost every project:
job_id(Primary Key): A unique identifier for the task.input_data(Text/JSON): The raw data for the job.status(Single-Select Field): With options like ‘Queued’, ‘Processing’, ‘Completed’, ‘Error’.output_data(Text/JSON): The final result.error_message(Text): Where to log what went wrong.last_updated(Timestamp): To see when things last moved.
Your orchestrator’s first step should be to pull a record from this base where status is ‘Queued’. Its second step is to immediately update that same record’s status to ‘Processing’. Now, that job is locked. No other run of the automation can grab it. If the process succeeds, the final step is to update the status to ‘Completed’ and paste in the output. If it fails? The status becomes ‘Error’ and the error message is logged.
This changes everything. Now you have a centralized view of your system’s health. You can build a separate automation that just looks for records stuck in ‘Processing’ for too long. You can create a view in Airtable of all ‘Error’ records and manually re-queue them with one click. You’ve built a resilient system, not just a script.
A Practical Example: Building a Research Agent
Let’s make this concrete. Imagine you want an agent that takes a company domain, finds the CEO’s name, and drafts a cold email introduction. Here’s how you’d build it using the principles above.
1. The Airtable Base (Our State Machine):
Create a table called ‘Outreach Jobs’ with the fields I listed above, plus a ‘company_domain’ field. To start, you add one record: `company_domain` = ‘deepusecase.com’, `status` = ‘Queued’.
2. The Make.com Scenario (Our Orchestrator):
The scenario runs every 5 minutes. Its first module is ‘Airtable: Search Records’. It looks for a single record in ‘Outreach Jobs’ where `status` is ‘Queued’.
3. The ‘Processing’ Lock:
The next module is ‘Airtable: Update a Record’. It takes the ID from the record it just found and immediately changes its `status` to ‘Processing’. This is the critical lock.
4. The Action Layer:
Now the real work begins. You might have a sequence of modules:
- An HTTP module that calls a scraping API (like ScraperAPI) to get the text from the company’s ‘About Us’ page.
- An OpenAI ‘Create a Completion’ module with a prompt like this:
"Scan the following text from the company's website and extract the full name of the CEO or President. If you cannot find it, return only the word 'NULL'.Website Text:
---
{text_from_scraper}
---CEO Name:"
- A Router module that checks the output from the LLM. If the output is ‘NULL’, it follows an error path. If it has a name, it continues.
- Another OpenAI module to draft the email, using the CEO’s name.
5. The Final State Update:
The final step in the main flow is another ‘Airtable: Update a Record’ module. It sets the `status` to ‘Completed’ and writes the drafted email into the `output_data` field.
If anything went wrong (the router caught a ‘NULL’ or another module failed), an error-handling route takes over. It uses one final ‘Airtable: Update a Record’ module to set the `status` to ‘Error’ and logs the specific error message from Make.com into the `error_message` field.
This isn’t a simple, sexy workflow. It has branches and error handling. But it’s durable. It’s an actual piece of production infrastructure. This is the difference between a demo and a tool you can run a business on.