How Modern AI Products Work: 7 Concepts Every Product Engineer Should Know

AI vocabulary is changing almost as quickly as the technology itself.

Terms such as agents, RAG, and MCP now appear regularly in product discussions, technical documentation, and company roadmaps. But knowing the definitions is only the beginning.

For product engineers, the more important question is:

How do these concepts affect the products we design and build?

This guide explains seven important AI terms through a practical product-engineering lens.

1. AI Agents

An AI agent is a system that works toward a goal by choosing actions, using tools, observing the results, and deciding what to do next.

A chatbot typically responds to one request. An agent may operate as a loop:

Goal → Plan → Act → Observe → Adjust

Consider an AI assistant investigating an increase in customer cancellations. It might:

• Query product analytics
• Search recent support tickets
• Compare customer segments
• Identify common complaints
• Produce a report with recommendations

The user defines the outcome, but the system determines some of the steps.

Why agents matter

Agents are useful when the path to an answer cannot be completely defined in advance.

However, autonomy also creates more opportunities for errors. If a normal workflow already solves the problem reliably, adding an agent may only increase cost, latency, and risk.

A useful rule is:

Use deterministic software when the steps are known. Use an agent when deciding the steps is part of the problem.

2. Reasoning Models

A reasoning model is designed for tasks involving planning, analysis, or multiple dependent steps.

These models are particularly useful for:

• Debugging complex software issues
• Comparing conflicting requirements
• Reviewing evidence
• Planning multi-step operations
• Solving technical or mathematical problems

They may spend more computation on a problem before returning an answer.

Why reasoning models matter

Not every task requires the most capable model.

A fast, inexpensive model may be enough for classification, extraction, or simple summarisation. A reasoning model may be better for difficult analysis.

Product teams should route requests based on complexity rather than sending every task to the largest model available.

The best model is not always the most powerful one. It is the model that provides the required quality at an acceptable cost and speed.

3. Embeddings and Vector Databases

An embedding is a numerical representation of content such as text, images, or audio.

Content with a similar meaning tends to receive a similar representation. This allows a system to find related information even when the wording is different.

For example:

• "I forgot my password."
• "I cannot access my account."

A traditional keyword search may treat these as different queries. An embedding-based search can recognise their semantic similarity.

A vector database stores and searches these numerical representations efficiently.

Why vector databases matter

Vector search supports features such as:

• Semantic search
• Recommendations
• Similar-document retrieval
• Duplicate detection
• Knowledge assistants

However, similarity does not guarantee correctness.

The most similar result could still be outdated, incomplete, or inappropriate for the user. Retrieval quality also depends on document structure, metadata, permissions, filtering, and evaluation.

The vector database is infrastructure. Relevance is a product-quality problem.

4. Retrieval-Augmented Generation

Retrieval-augmented generation, usually called RAG, gives a language model access to information outside its original training data.

A basic RAG system works like this:

Question → Retrieve relevant information → Add it to the model's context → Generate an answer

Imagine an employee asking:

"What is our parental-leave policy?"

A RAG system could retrieve the relevant section from the latest employee handbook and use it to create an answer with a citation.

This is particularly useful when information is:

• Private
• Specialised
• Frequently updated
• Specific to an organisation

Why RAG matters

RAG does not automatically make an AI system accurate.

A production system still needs reliable document ingestion, search, permissions, citations, freshness controls, and testing.

When a RAG answer is wrong, ask two separate questions:

1. Did the system retrieve the correct information?
2. Did the model interpret that information correctly?

This distinction helps teams fix the real problem instead of repeatedly adjusting prompts.

5. Model Context Protocol

The Model Context Protocol, or MCP, provides a standard way for AI applications to connect to external data and tools.

Through these connections, an AI application might:

• Search internal documentation
• Read a file
• Query a database
• Create a calendar event
• Update an issue tracker
• Perform a controlled business action

Without a shared standard, developers may need to build a separate integration for every combination of AI application and external service.

Why MCP matters

MCP can make integrations more reusable, but it does not remove the need for security.

Every tool still requires:

• Authentication
• Authorisation
• Input validation
• Clear permissions
• Audit logs
• Error handling
• Human approval for sensitive actions

A model that can recommend an action is useful. A model that can perform irreversible actions without clear boundaries is dangerous.

AI tools should be designed like production APIs: narrow, predictable, observable, and permission-aware.

6. Mixture of Experts

A mixture-of-experts, or MoE, model contains multiple neural sub-networks known as experts.

A routing mechanism activates only some of these experts for each input. This allows a model to have a large overall capacity without using every parameter for every request.

The term expert can be misleading. These components do not necessarily correspond to understandable roles such as "coding expert" or "finance expert." Their specialisation is learned during training.

Why mixture of experts matters

Most product engineers will not design MoE architectures themselves.

Instead, they will experience the effects through:

• Model quality
• Inference cost
• Latency
• Throughput
• Deployment requirements

This is one reason parameter count alone is not enough to judge a model.

What matters is how well the model performs on your actual use case.

7. AGI and ASI

Artificial general intelligence, or AGI, generally refers to a hypothetical AI system with broad capabilities across many cognitive tasks.

Artificial superintelligence, or ASI, describes a theoretical system whose abilities would substantially exceed human capabilities across many domains.

Neither term has a universally accepted definition or test.

They are useful concepts for discussing the future of AI, but they are not practical product requirements.

Why AGI and ASI matter

Product teams should build around demonstrated capabilities, not predictions about future intelligence.

Instead of asking:

"Is this model approaching AGI?"

Ask:

• Which tasks can it complete reliably?
• Under what conditions does it fail?
• How much autonomy should it have?
• Which actions require approval?
• How will we evaluate its behaviour?

These questions lead to better engineering decisions today.

How these concepts fit together

These seven terms describe different layers of an AI product.

Reasoning models and MoE relate to the model and how it processes tasks.

Embeddings, vector databases, and RAG help the system find and use relevant knowledge.

MCP connects the application to external tools and services.

Agents coordinate actions toward a goal.

AGI and ASI describe possible future levels of general capability.

A simplified system might look like this:

User request → Select an appropriate model → Retrieve relevant knowledge → Access external tools → Perform an action → Validate the result → Ask for human approval when necessary

The model is only one part of the product.

Permissions, evaluations, observability, user experience, fallback behaviour, and cost controls are what turn an impressive prototype into a dependable system.

What I would prioritise as a product engineer

For teams building AI products today, I would prioritise three concepts:

RAG

Because useful AI products often need access to private, specialised, or current information.

MCP and tool design

Because AI becomes more valuable when it can interact safely with real systems.

Agents

Because they introduce both significant opportunities and significant risks through greater autonomy.

Reasoning models support all three, while MoE, AGI, and ASI are important concepts to understand but are less likely to determine an application-level architecture decision.

Final thoughts

The strongest AI products will not necessarily use the largest model or the most autonomous agent.

They will use the right level of intelligence for the problem, provide relevant context, offer carefully designed tools, and place clear boundaries around what the system can do.

As a product and AI engineer, this is the area I find most interesting: translating emerging AI capabilities into products that are useful, measurable, secure, and reliable.

Because the real engineering challenge is not making AI look impressive in a demo.

It is making AI work responsibly in production.