Why I Think Most AI Agents Are Overengineered

AI agents are everywhere.

Multi-agent systems.

Agent swarms.

Autonomous teams.

Planning agents.

Self-improving agents.

It seems every week a new framework appears promising to build the next generation of autonomous AI systems.

After spending considerable time studying and experimenting with AI workflows, I have come to a simple conclusion:

I think most AI agents are overengineered.

That doesn't mean agents are useless.

Far from it.

I simply believe many builders are solving problems with agents that could be solved with something much simpler.

The Industry Loves Complexity

Let's imagine you want to build a system that:

1. Reads PDFs.


2. Extracts information.


3. Stores embeddings.


4. Answers questions.

I've seen builders create architectures like this:

Research Agent

↓

Planner Agent

↓

Retriever Agent

↓

Memory Agent

↓

Answer Agent

↓

Reviewer Agent

Six agents.

Multiple prompts.

Complex state management.

Retries.

Memory synchronization.

And a lot of headaches.

Meanwhile, the same problem can often be solved with:

PDF → Chunk → Embed → Vector DB → LLM → Response

Sometimes a workflow is enough.

Not everything needs an agent army.

Workflows Solve Most Problems

In my experience, most AI applications are deterministic.

They follow a sequence:

Input

↓

Transform

↓

Retrieve

↓

Generate

↓

Output

Examples include:

• Document Q&A


• Customer support


• Meeting summaries


• Blog generation


• Code review


• Knowledge assistants

These are workflows.

Not autonomous systems.

And workflows are:

• Easier to debug


• Easier to scale


• Easier to maintain


• Easier to explain

Complexity should be earned, not assumed.

Agents Introduce Hidden Costs

Every additional agent brings:

More prompts

Which means more tokens.

More latency

Each step adds execution time.

More hallucination opportunities

One bad output propagates downstream.

More debugging pain

Finding failures becomes difficult.

More infrastructure complexity

Memory, orchestration, retries, and monitoring become necessary.

What started as a simple application suddenly becomes an engineering project.

Most Builders Don't Need Multi-Agent Systems

Let's compare.

Simple Workflow

documents → embeddings → Chroma → GPT → answer

Simple.

Reliable.

Fast.

Now compare that to:

Planner Agent

↓

Retriever Agent

↓

Research Agent

↓

Critic Agent

↓

Memory Agent

↓

Final Writer Agent

Do you really need six agents to answer questions from a PDF?

Probably not.

Where Agents Actually Shine

I'm not anti-agent.

I think agents are powerful when:

Long-running tasks exist

For example:

• Researching across multiple websites


• Monitoring APIs


• Scheduling actions


• Autonomous coding loops

Decision-making is required

For example:

if bug_found:

    fix_code()

elif tests_fail:

    rerun()

else:

    deploy()

Human intervention matters

Human-in-the-loop systems benefit greatly from agent architectures.

Multiple tools must collaborate

Email.

GitHub.

Slack.

Databases.

Web search.

This is where agents become interesting.

I Believe Workflows Matter More Than Agents

One thing I've learned is that builders often jump directly into agent frameworks.

CrewAI.

LangGraph.

AutoGen.

And many others.

But before building agents, I think we should first ask:

Can a workflow solve this?

If the answer is yes, start there.

Only introduce agents when complexity demands them.

Not because Twitter says agents are the future.

In fact, I recently shared some of my favorite repositories in:

"7 GitHub Repositories I Recommend to Every AI Builder"

Some of those tools are incredibly powerful—but power doesn't always mean more complexity.

Sometimes the best architecture is the simplest one.

The Software Industry Has Seen This Before

Microservices.

Kubernetes.

Distributed systems.

Event-driven architectures.

Many teams adopted them before they truly needed them.

AI may be repeating the same pattern.

Builders see impressive demos and assume every project needs:

• Agent memory


• Multi-agent orchestration


• Planning loops


• Reflection agents

But complexity isn't innovation.

Complexity is cost.

My Rule

I follow a simple principle:

• Workflow first.


• Agent second.


• Multi-agent last.

Start with the simplest architecture possible.

Only add complexity when reality demands it.

Not because hype demands it.

Final Thoughts

AI agents are exciting.

Frameworks like LangGraph and CrewAI are pushing the ecosystem forward.

And I believe autonomous systems will play a major role in the future.

But today, I think many AI builders are overengineering solutions.

Most problems don't require a team of agents.

Most problems require clear workflows.

Because at the end of the day, users don't care whether your application has twelve agents.

They care that it works.

And in engineering, simplicity is often the most underrated feature.