From API Users to Embedded Agents: How Systems Are Becoming Smarter From Within

We often think of software systems as things we can talk to, through APIs, dashboards, or scripts.

But what if software didn’t just listen to requests, but started to participate?

That is the shift we are now seeing.
Instead of calling systems through APIs, we are beginning to embed agents — small, autonomous modules, often AI-powered — that live inside the system and act on their own.

Doorbells vs Housemates

Here is a simple analogy:

• With an API, you press a doorbell. You ask for something, get a reply, and move on.
• With an agent or embedded component, you are not ringing the bell, you are living in the house. You help with the chores, notice when something needs doing, and sometimes take initiative yourself.

This changes the whole structure.

You no longer have a passive system waiting for commands.
Instead, you have an ecosystem in which different components act, respond, and evolve together.

A Concrete Example: Translating Blog Posts

Suppose you want your website to offer multilingual versions of every article.

Traditional (API-based) approach:

• You use a tool like Make.com.
• It fetches new posts through a REST API.
• It sends them to DeepL or another translator.
• It posts the translated content back.

Your website knows nothing about the process. It just responds to requests.

Embedded agent approach:

• You embed a translation agent into the site.
• It listens for new posts from within the system.
• It knows when to translate, can handle errors, apply custom rules, or collaborate with other agents, for example a glossary module or SEO optimiser.
• It grows with the system. You might later teach it to prioritise certain authors or languages.

The goal is the same, but the structure is very different.

Why This Matters

This is more than a technical adjustment. It reflects a shift in how we think:

• From external control to internal capability
• From functions to roles
• From one-way requests to shared awareness and interaction

Systems designed in this way behave more like living environments. They can:

• React to events and changing conditions
• Adapt to new modules or goals
• Coordinate internally, without relying on central control

What Makes an Embedded Agent?

A few characteristics stand out:

1. It lives inside the system
   Not on the outside, but as part of the environment.
2. It has some autonomy
   It can initiate actions, rather than only respond to commands.
3. It follows shared rules or protocols
   These define how it interacts with other parts. (This is where MCPs, or Modular Component Protocols, may come in.)
4. It can evolve or be replaced
   Agents can be updated, extended, or removed, without rewriting the whole system.

A Word on MCPs

You may have heard the term MCP (Modular Component Protocol).
This is just one way of structuring systems so that components can:

• Discover each other
• Register their capabilities
• Coordinate actions and decisions

MCPs are only one architectural tool among others. The more important idea is this:

We are building systems that operate less like machines and more like collaborative teams.

Whether through MCPs, plugins, local LLMs, or embedded agents, the direction is clear.
Modern software is becoming cooperative.

Who’s Already Doing This?

You have probably used systems built along these lines:

• VS Code extensions register themselves and operate inside the editor.
• WordPress plugins participate in publishing logic, not just display content.
• Browser extensions interact with page content in real time.
• Chatbots and voice agents increasingly act as assistants, not just interfaces.
• Local LLMs on phones or desktops now observe context and adjust behaviour accordingly.

These are all examples of the same shift.
We are moving from request-response interactions to embedded, context-aware participation.

Why Now?

The reason is simple: AI is accelerating this change.

As models grow more capable, it is no longer enough to send data back and forth. We want systems that can:

• Stay engaged in the loop
• Observe and adapt to what is happening
• Behave in ways that feel intelligent, rather than just mechanical

To support that, we need systems designed not just around APIs, but around interaction, cooperation, and modular intelligence.

Closing Thought: Designing for Participation

We used to write software that waited for instructions and returned results.

Now we are starting to build systems that listen, respond, suggest, and adapt, not from the outside, but from within.

This is not just a new technical approach. It is a different way of thinking.
And if we want to build systems that grow with us, not just work for us, it is worth learning to think this way.