What Building an AI Agent Taught Me About Backend Engineering

What Building an AI Agent Taught Me About Backend Engineering

Building an AI agent initially appears to be an exercise in working with language models.

In practice, much of the work quickly becomes familiar backend engineering.

Requests need validation.

State needs to be managed.

External services need timeouts.

Failures need to be handled.

Operations need to be observable.

Components need clear boundaries.

The language model adds a new dimension, but many of the underlying engineering principles remain the same.

The Model Is Not the Application

One of the most useful lessons is that an LLM should be treated as a component rather than the entire system.

The model can provide:

• reasoning;
• classification;
• planning;
• language generation;
• tool selection.

The surrounding application should provide:

• state;
• permissions;
• validation;
• execution;
• persistence;
• observability;
• failure handling.

This separation creates a much stronger architecture.

Orchestration Matters

An agent is fundamentally an orchestration problem.

A typical execution looks like:

Goal
 ↓
Plan
 ↓
Action
 ↓
Observation
 ↓
Next Action
 ↓
Completion

The challenge is maintaining control over that loop.

The system must know when to continue and when to stop.

That is very similar to designing other workflow-oriented backend systems.

APIs Still Matter

Even when an application is centered around AI, clean interfaces remain important.

Tools should have clear contracts.

Internal services should have predictable inputs and outputs.

Errors should be explicit.

Configuration should not be scattered throughout the codebase.

The AI layer does not remove the value of good API design.

If anything, it makes it more important.

State Is a Backend Problem

Agent memory initially sounds like an AI-specific problem.

After working with it, it becomes clear that much of it is state management.

The system needs to answer:

What is the task?

What has already happened?

What information is relevant?

What should happen next?

What can be discarded?

What must persist?

Those questions are familiar to backend engineers.

The difference is that an agent may make decisions about those transitions using a language model.

Failure Is Normal

External systems fail.

That is not specific to AI.

An agent simply introduces more external dependencies.

A realistic execution may involve:

User
 ↓
Application
 ↓
LLM
 ↓
Tool
 ↓
HTTP API
 ↓
Database

Every boundary can fail.

Designing around those failures from the beginning produces a much more resilient system.

Observability Becomes Even More Important

Traditional backend services already benefit from logs and metrics.

Agents need them even more because their behavior can involve multiple reasoning steps.

When an agent produces an unexpected result, an engineer may need to answer:

• What did the user ask?
• What did the model decide?
• Which tool was selected?
• What arguments were generated?
• What did the tool return?
• How many iterations occurred?
• Where did execution diverge?

Without telemetry, answering those questions becomes guesswork.

Simplicity Wins

It is tempting to build increasingly complicated agent architectures.

More agents.

More tools.

More memory.

More orchestration layers.

More frameworks.

But complexity should have a reason.

A simple single-agent workflow with a small set of well-designed tools can be more useful and maintainable than a complicated multi-agent system with poorly defined boundaries.

The architecture should grow with the problem.

Frameworks Are Not the Architecture

AI development currently has a large ecosystem of frameworks.

They can accelerate development, but they do not replace architectural thinking.

A framework can help with:

• model integration;
• tool calling;
• workflow execution;
• memory;
• tracing.

It cannot decide what the application actually needs.

Understanding the underlying execution model remains more valuable than memorizing a particular framework’s API.

Testing Requires a Different Mindset

Traditional deterministic tests often expect one precise output.

AI systems require more flexible evaluation.

The important questions can instead be:

• Did the correct tool get selected?
• Were unsafe operations prevented?
• Did the workflow terminate?
• Was invalid input rejected?
• Did the system recover from failure?
• Was the final result useful and grounded?

The test target becomes the behavior of the system rather than a single generated sentence.

Engineering Around Uncertainty

The biggest conceptual difference is uncertainty.

A normal function might behave like:

Input → Output

An AI component may behave more like:

Input
  ↓
Probabilistic Decision
  ↓
Possible Actions
  ↓
Validation
  ↓
Controlled Execution

That means deterministic boundaries become especially valuable.

The system should not depend on the model being perfect.

It should be designed to remain safe and useful when the model is imperfect.

The Broader Lesson

Building an AI agent reinforced something important about backend engineering:

The difficult part of software is rarely just writing code.

The difficult part is designing boundaries.

Where does responsibility belong?

What information should be shared?

What happens when a dependency fails?

How does the system recover?

How do we observe it?

How do we test it?

How do we keep complexity under control?

AI makes those questions more visible because the system contains a probabilistic component.

Final Thoughts

Building an AI agent is not simply an exercise in prompt engineering.

It is an opportunity to practice software architecture under uncertainty.

The strongest lessons are familiar ones:

• keep responsibilities separated;
• make state explicit;
• define clear interfaces;
• validate external input;
• bound execution;
• design for failure;
• instrument important operations;
• test behavior;
• keep the architecture understandable.

The language model may be the most visible part of the system.

But the engineering around it is what determines whether the system becomes a useful application or merely an impressive demonstration.