Last month I needed to spin up a custom lead‑qualification agent that could pull data from a web form, enrich it with a LinkedIn scrape, and then send a personalized email — all without writing a single line of server‑side code.
By the end of this guide you’ll have a working AI agent builder platform that lets you chain prompts, tools, and memory in a visual flow, and you’ll know exactly where the cheap‑to‑build version falls short versus a paid blueprint.
Scenario: Building a Lead‑Qualification Agent
I started with a simple goal: when a prospect fills out a contact form on my site, an AI agent should read the message, look up the person’s LinkedIn profile, summarize their role, and draft a cold email that mentions a recent post they made. The whole thing had to run on a schedule, store the output in a Google Sheet, and ping me on Slack if the confidence score dropped below 70%.
I broke the flow into three blocks: (1) a trigger that watches the form, (2) a reasoning loop that calls an LLM with tools, and (3) an output stage that writes the sheet and sends the Slack alert. The trigger was easy — I used the Make platform’s webhook module. The reasoning loop needed a tool‑calling LLM, so I hooked up the OpenAI Assistants API with a custom function for the LinkedIn scrape. The output stage used Make’s Google Sheets and Slack modules.
At first glance the pieces seemed to snap together, but the first run exposed a classic problem: the agent kept looping on the same LinkedIn URL because the scrape tool returned an error and the LLM kept trying to “fix” it by calling the tool again with the same parameters.
What most guides get wrong about chaining prompts
Many tutorials treat the LLM as a black box that will magically follow a chain of prompts if you just feed it the previous output. They skip the part where you need to explicitly tell the model when to stop calling a tool and when to accept a failure as final. In my early version the LLM saw the scrape error, decided the data was “incomplete,” and re‑invoked the scrape tool with identical arguments, creating an infinite loop that burned through my OpenAI quota in minutes.
The fix is simple but often omitted: add a “stop” condition in the tool definition. I gave the scrape function a boolean output called success. In the assistant’s instructions I wrote: “If success is false, return the error message to the user and do not call any further tools.” That single line stopped the recursion.
How to debug when the agent loops or hallucinates
When the agent misbehaves, the first place to look is the tool call log. Make.com shows each module’s input and output; the OpenAI assistant logs show the function name, arguments, and returned value. I keep a temporary Google Sheet that logs every tool call with a timestamp, the arguments sent, and the raw response. If you see the same arguments appearing twice in a row, you have a loop.
Hallucinations show up differently: the LLM will return a plausible‑looking LinkedIn URL that doesn’t exist, or it will invent a job title. To catch those, I added a validation step after the scrape: a simple regex that checks the URL format and a second call to a lightweight SERP API to verify the profile actually resolves. If the validation fails, the agent writes “VERIFICATION FAILED” to the sheet and moves on.
One concrete gripe: the OpenAI Assistants API still returns vague error messages like “Internal server error” when you exceed the rate limit, with no hint to back off. I lost an hour debugging a loop that was actually just a 429 hidden behind that generic message.
One concrete love: the ability to version the assistant’s instructions as a separate text file in Git. I can tweak the prompt, push a commit, and redeploy the assistant without touching the workflow — makes experimentation feel safe.