# Composite AI

Artificial Intelligence is having a moment.

Every week there seems to be a new model, a new benchmark, or a new headline declaring that AI can now do everything from writing software to passing professional exams. The excitement is justified: modern generative AI systems are remarkable tools. But there is an important architectural lesson that often gets lost in the hype:

**Real-world AI systems rarely consist of just one AI.**

The most effective systems combine multiple techniques, multiple tools, and multiple kinds of AI. Increasingly, we are seeing a pattern emerge that many people call **Composite AI**. Composite AI systems combine multiple approaches to intelligence in order to solve problems more reliably, more transparently, and often more safely than any single technique alone.

And interestingly, the core idea behind Composite AI is not entirely new.

### Multiagent Systems

My doctoral research was in **multiagent systems**. At the time, today’s generative AI systems did not exist. Large language models, prompt engineering, and ChatGPT-style interfaces were still many years in the future.

But even then, we were already thinking about AI systems in terms of **multiple cooperating and competing agents**. Rather than asking one large algorithm to solve an entire problem, we decomposed problems into pieces. Each agent had a specific skill and role, and the agents interacted with one another to produce a solution. Looking back, the architectural mindset of multiagent systems maps very naturally onto what we now call Composite AI.

The technologies have changed.
The architectural principles have not.

### What Is Composite AI?

I think about **Composite AI** across three dimensions:

Composite AI systems are typically:

– **Multiagent**
– **Multi-tool**
– **Multi-AI**

Let’s unpack each one.

#### Multiagent

A Composite AI system is usually built as a **graph of agents**, with a linear chain being a particularly simple example. Each agent performs its specific task, and the agents collaborate to solve the overall problem. Rather than asking a single AI to solve the entire business problem, we instead design a workflow like this:

>Agent A → Agent B → Agent C

Each agent receives inputs from the previous step, performs its function, and produces outputs for the next step

This approach offers several advantages:

– systems become **modular**
– individual components can be **improved independently**
– reasoning becomes **easier to understand**

And importantly, failures are easier to diagnose. When a system breaks, you ask: “Which agent misbehaved?”, instead of staring at one giant black box.

#### Multi-tool

Composite AI systems also use **multiple tools**.

Each agent can use tools via MCP (Model Context Protocol) to interact with:

– databases
– APIs
– spreadsheets
– knowledge graphs
– search engines
– statistical models
– simulation engines

Think of it less like a robot brain and more like a **well-equipped workshop**.

The agent doesn’t just “think.” It **uses tools**.

#### Multi-AI

Perhaps the most important idea is this: **Different types of AI are good at different things.**. Generative AI is exceptional at:

– interpreting messy language
– summarizing information
– interacting with humans

But traditional AI techniques remain incredibly powerful for:

– optimization
– forecasting
– classification
– anomaly detection
– simulation

Many traditional machine learning algorithms also provide:

– **optimality guarantees**
– **explainable results**
– **transparent decision processes**

And perhaps most importantly: Traditional ML models **do not hallucinate**. They may be wrong, but they are not imaginative.

These different AI’s can be full fledged agents or invoked via the MCP protocol.

### A Simple Composite AI Pattern

Consider a simple network architecture with three-stages:

This pattern allows generative AI and traditional AI to each do what they do best.

1. The first agent uses **Generative AI**. Its job is to take in unstructured **messy, noisy, real-world inputs**. For example:

– emails
– reports
– documents
– sensor logs
– notes
– transcripts

These inputs are rarely neat and tidy. A generative AI model can transform them into structured outputs such as:

– JSON
– tables
– standardized fields
– extracted entities

Importantly, this step requires only a **small cognitive leap**. The AI is not being asked to solve the entire business problem. It is simply translating information into a structured format.

Because the task is limited in scope, **hallucinations are reduced**. Not eliminated. But reduced.

And once the information is structured, it can be:

– **validated by humans**
– **checked with rules**
– **stored in databases**
– **passed to downstream systems**

1. The second agent uses **traditional AI or ML** which might include:

– gradient boosting models
– random forests
– linear models
– optimization algorithms
– simulation engines
– statistical models

At this stage the input data is clean and structured, which allows these algorithms to perform their work effectively and efficiently. Many of these techniques offer advantages that generative AI does not:

– statistical guarantees
– explainability
– repeatability
– strong performance on numerical problems

This stage is where the **core decision-making logic** usually lives.

1. The final agent uses GenAI again to translate structured results back into **human-friendly explanations**. For example: Instead of presenting:

>“`
risk_score = 0.82
recommended_action = reduce exposure
confidence = 0.91
“`

the final agent might generate:

> Based on recent transaction activity and historical behavior patterns, the model estimates an elevated risk level. We recommend temporarily reducing exposure while monitoring for additional signals.

This step helps bridge the gap between **machine reasoning** and **human decision-makers**. It turns structured outputs into **narrative insight**. And humans tend to like narratives.

### Why Composite AI Works

Composite AI works well because it acknowledges a simple truth:

> No single AI technique is good at everything.

Instead of searching for a magical all-purpose model, we assemble **specialists**.

Think of it like a medical team. You wouldn’t ask a single doctor to perform surgery, interpret MRI scans, prescribe medication, and run the hospital. You assemble a **team of experts**. Composite AI follows the same philosophy.

### A Few Practical Benefits

Organizations adopting Composite AI often see improvements in:

– **Reliability:** Breaking a system into smaller tasks reduces the risk of catastrophic failure.
– **Transparency:** Individual components are easier to explain.
– **Governance:** Structured intermediate outputs make auditing easier.
– **Incremental Improvement:** Agents can be upgraded independently.
– **Safety:** Hallucination risks can be contained within specific stages.

### A Quiet Shift in AI Architecture

Over the next few years, we will see a shift in how AI systems are designed.

Early generative AI applications often looked like this:

> One big prompt → One big answer.

Composite AI systems instead look like this:

> Agent → Agent → Agent → Decision

This architectural shift is subtle, but important. The real power of AI will not come from one enormous model trying to do everything. It will come from **well-designed systems where multiple forms of intelligence collaborate**.

The idea behind Composite AI is simple: Use the **right intelligence for the right job**.

Let generative AI interpret messy human information. Let traditional algorithms perform precise reasoning. Let structured systems enforce rules and constraints. And let the whole system work together.

It turns out that intelligence—artificial or otherwise—often works best as a **team sport**.

### A Small Suggestion

If your organization is exploring AI, consider starting with a simple question:

> _Which business decisions could improve if the right data and analysis were available faster?_

Those decisions often become the starting point for practical Composite AI systems.

If you’d like to explore how these architectures might apply in your organization, feel free to reach out.

Sometimes the best way to begin with AI is not with a giant leap—but with a few thoughtful steps in the right direction.