Oct 15, 2025
TL;DR
The world of Artificial Intelligence is filled with buzzwords. This guide cuts through the noise to clarify what these terms actually mean and how they apply to your business. We’ll explore the different types of AI models, specifically Large Language Models (LLMs), explain the common pitfalls that lead to the “70% problem,” and provide clear guidelines for building AI agents that truly grow the value of your business via an easy-to-follow practical example.
What are LLMs? A simple analogy
Think of LLMs like graduates entering the workforce. They start with a broad education and can specialize over time to become experts in a specific field.
Foundation Models: The University Graduate
These are large, general-purpose models trained on a massive, diverse dataset (like the entire internet). They are intelligent on a wide variety of subjects, adaptable, and can learn new skills. These are the big names you’ve probably already heard about, like OpenAI’s GPT series (ChatGPT), Anthropic’s Claude, Meta’s Llama, Google’s Gemini.
Frontier Models: The Top of the Class
This is not a different type of model, but rather a term for the subset of foundation models that are the most powerful, cutting-edge, and state-of-the-art available. Think of them as the valedictorians of the graduating class. These are the very latest releases, like GPT-5, Claude 4, or Gemini 2.5.
Fine-Tuned Models: The PhD Specialist
A fine-tuned model is a foundation model that has undergone additional training on a smaller, specific dataset to become an expert in a particular domain. Just as a graduate pursues a PhD to specialize, a foundation model is fine-tuned to excel at a single task. A general model like ChatGPT could be fine-tuned on thousands of medical texts to create a highly accurate medical chatbot for a hospital (e.g. OpenEvidence).
What about “Agents”?
If a foundation model is a graduate with a world of knowledge, then an AI Agent is that graduate hired for a job. An agent doesn’t just answer questions; it takes action. It uses the model’s intelligence to perform tasks, use tools (like your email or calendar), and work autonomously towards a goal you’ve set. You’re probably hearing this term a lot right now.
The unrealistic expectations of AI: The “70% Problem”
Many businesses are disappointed when they first deploy an AI agent. They task a powerful, general-purpose foundation model with a complex workflow, only to find it completes the job correctly about 70% of the time. This gap between expectation and reality is the “70% problem.” The causes are predictable:
Unrealistic expectations:
Expecting a single “do-it-all” agent to flawlessly handle a multi-step, nuanced business process.
Poor task definition:
Giving the AI a vague or overly broad goal, such as “manage my customer support,” without breaking it down into specific, measurable actions.
Bad data quality:
Feeding the AI messy, inconsistent, or incomplete information, which leads to unreliable outputs.
How to Overcome the 70% Problem: A Practical Strategy
The key is to move away from a single, agent and instead build a team of specialized agents that work together. By breaking down a large workflow into a series of simple, well-defined tasks, you can achieve much stronger and more successful automation. You don’t hire a single employee to handle an entire business, why hire a single agent for it?
Focus on streamlining simple yet tedious tasks. Instead of asking one agent to "handle the new client pipeline," create a chain of agents, each with one specific job.
Example: Automating Inbound Service Requests
Let's say you run a services business where new jobs come in via email. Your manual workflow looks like this: Read Email -> Understand Request -> Ask Questions -> Build Invoice -> Send Invoice -> Schedule Service.
Instead of one agent, you should deploy a team:
Email parsing agent:
Its only job is to read an incoming email and extract key pieces of information (e.g., customer name, service requested, contact info). This is a simple, high-accuracy task.
Quoting agent:
This agent takes the structured information from the first agent and generates a standardized invoice or quote based on your pricing rules.
Customer confirmation agent:
This agent sends the quote to the customer and is programmed to understand simple “yes/no” responses or ask clarifying questions if necessary.
Scheduling agent:
Once the quote is approved, this agent accesses your calendar and offers available time slots to the customer to finalize the booking.
By breaking down the complex process, each agent performs a high-success-rate task. The combined result is a reliable, efficient, and fully automated workflow that pushes past the 70% barrier and delivers real business impact.
Closing thoughts - keep it simple!
When it comes to AI agents, simplicity is your superpower. A single agent assigned a complex, multi-step workflow will always have more points of failure than a team of specialized agents. An agent with a narrow, well-defined task, like “extract the invoice number from this PDF,” will perform with extremely high accuracy. In contrast, an agent tasked with “manage my accounts payable” has to interpret context, make multi-step decisions, and handle exceptions, dramatically increasing the chance of error.
The secret to powerful AI automation isn’t finding one genius agent; it’s building a disciplined assembly line of focused specialists. At Substrate, we do this all the time - we grow the enterprise value of SMBs by developing and implementing simple, easy-to-use, and impactful technology. Plainly, we build AI that just works.
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