In the last year, the word “agent” has been used to describe almost anything that includes a language model. However, in the world of software development and production systems, a true Artificial Intelligence Agent is a much more complex and interesting piece of architecture than a simple chatbot connected to an OpenAI API with a long “system prompt”.

The Chatbot Illusion

When a user interacts with ChatGPT, they are interacting with a model that processes text and returns text. The model doesn’t “do” anything other than guess the next most likely word based on its training and context window. If you ask a generic chatbot to check your store’s stock, it will reply that, as a language model, it doesn’t have access to the internet or private databases.

An AI Agent, on the other hand, is designed from its core to be action-oriented. It is equipped with an arsenal of tools and functions (Function Calling) that allow it to observe the outside world, interact with it, modify its state, and return with results.

Architecture of a true Agent

To build a useful agent in B2B environments or for process automation, we need an architecture that includes:

  1. Reasoning Engine (LLM): The “brain” that makes decisions about which tools to use.
  2. Short and Long-Term Memory: To maintain the context of the current session and remember historical preferences or structured information from a vector database (RAG).
  3. Tool Catalog: Deterministic functions (Python/Node code) that the agent can execute. From searching the database to sending an email or processing a payment.
  4. Rules and Guardrails: Strict logic that prevents the agent from performing destructive actions or responding outside its domain.

Stateful execution

The most noticeable difference for the end user is that the agent doesn’t just converse, it solves. When a customer enters an ecommerce site and tells the agent “I need to cancel my last order”, the agent doesn’t reply “Please call support”. The agent autonomously:

  • Uses the get_user_session tool to identify the customer.
  • Uses the fetch_recent_orders tool to search the Shopify or WooCommerce database.
  • Evaluates if the order is within the cancellation time frame according to business rules.
  • Uses the cancel_order tool via an API call.
  • Informs the user that their order has been successfully canceled and sends the refund receipt.

The end of the “Wrappers”

As the technology matures, companies realize that connecting a text input to a generic API provides no real value. The true value lies in systems engineering: in how we orchestrate the language model within a secure, predictable digital ecosystem that is highly integrated with our operational tools (CRMs, ERPs, Databases).

That is the work I am passionate about: building systems, not just prompts.


Back to Archive