DEV Community: Jaideep Parashar

Your Dashboard Is Green. Your Users Are Still Unhappy.

Jaideep Parashar — Fri, 16 Oct 2026 13:42:51 +0000

Imagine getting a message from a customer:

"Your application isn't working."

You open your monitoring dashboard.

Server uptime: 99.99%.

API response time: 120 milliseconds.

Error rate: 0.1%.

CPU usage: normal.

Database connections: healthy.

Everything is green.

So you respond:

"Our systems are working normally."

But the customer is still unable to complete their task.

And now you have an interesting problem.

Your monitoring system says everything is fine. Your user says nothing is fine.

Who is right?

I think this reveals something we don't discuss enough in software engineering.

A system can be technically healthy and still be completely unsuccessful.

We Measure What Is Easy to Measure

Software engineering has become remarkably good at collecting metrics.

We measure:

uptime
latency
throughput
error rates
memory consumption
CPU utilization
database performance
deployment frequency
test coverage

These are important measurements.

They help engineers understand how software behaves.

But they don't necessarily tell us whether the software is achieving its purpose.

Consider an online shopping application.

The homepage loads.

The search API responds.

The product database works.

The checkout service is available.

But customers cannot complete their purchases because a confusing validation message prevents them from entering their delivery address.

From the infrastructure perspective, almost everything might look healthy.

From the customer perspective, the application is broken.

The system is available. The outcome is unavailable.

That's a very different failure.

The Problem With Green Dashboards

A green dashboard creates confidence.

Sometimes deserved confidence.

Sometimes false confidence.

Imagine a monitoring panel showing:

Service Health

API Uptime 99.99%
Avg. Latency 120ms
Error Rate 0.10%
CPU Usage 42%
Database Healthy

Overall Status: GREEN

Now imagine another dashboard:

User Outcomes

Checkout Completion 61%
Payment Retries High
Search Abandonment Rising
Failed User Journeys Rising

Overall Status: WARNING

Both dashboards can describe the same application.

Neither is necessarily wrong.

They're answering different questions.

The first asks:

"Is the infrastructure operating?"

The second asks:

"Can users accomplish what they came here to do?"

We need both.

The mistake is treating the first as proof of the second.

A Successful API Call Can Still Be a Failed Experience

This is one of the most interesting distinctions in modern software.

Consider an API:

@app.get("/recommendations")
def get_recommendations():
    return {
        "status": "success",
        "recommendations": []
    }

Technically, the API works.

It returns a valid response.

The HTTP status is 200.

No exception occurs.

Your monitoring system may count this as a successful request.

But if the user was expecting useful recommendations, the response might represent a product failure.

This doesn't mean an empty list is always wrong. Sometimes there genuinely are no suitable recommendations.

It means technical success and useful success are different concepts.

The application needs a way to distinguish them.

And that distinction cannot always be inferred from the HTTP status code.

This Gets More Complicated With AI

Now let's introduce an AI system.

Imagine a customer-support assistant.

It responds to every request.

Its average response time is excellent.

The model API rarely fails.

Token usage is within budget.

All technical indicators look good.

But customers keep receiving answers that are polite, grammatically correct, and completely unhelpful.

The system is functioning.

The model is responding.

The workflow is completing.

Yet the customer is frustrated.

This is why measuring an AI application only through latency, availability, and cost is insufficient.

We also need to understand things like:

Did the response answer the question?
Was it factually supported?
Did the system follow the required process?
Did the customer achieve their intended outcome?
Was human intervention required?
Did the interaction create another problem?

Some of these require carefully designed evaluation datasets, human review, or validated automated checks.

You cannot simply install a monitoring library and expect it to understand customer satisfaction.

Observability tells you what happened. Evaluation helps you determine whether it was good enough.

Both matter.

Sometimes Our Metrics Reward the Wrong Behavior

Here's an uncomfortable thought.

What if our measurements are not just missing problems?

What if they're encouraging them?

Imagine a development team being evaluated mainly on the number of features delivered.

Naturally, the team becomes very good at shipping features.

More releases.

More pull requests.

More completed tickets.

But perhaps the application becomes increasingly difficult to use.

Maybe customers only needed three improvements, while the team delivered thirty features.

The delivery metrics look impressive.

The product becomes worse.

The same thing can happen with AI.

If we reward a system for answering questions quickly, it may produce fast but incomplete responses.

If we optimize only for reducing human involvement, we may discourage necessary escalation.

If we optimize solely for low cost, we may sacrifice quality.

This is not an argument against metrics.

It's an argument for understanding what a metric actually represents.

When a measurement becomes a target, we need to be careful about what behavior it encourages.

The Missing Layer: Outcome Observability

I think software teams need to pay more attention to what we might call outcome observability.

Traditional observability helps us understand the internal state of a system through signals such as logs, metrics, and traces.

Outcome observability extends the question.

Instead of looking only at whether components operated correctly, we also look at whether important user journeys succeeded.

Consider an application that processes invoices.

We could measure:

Technical metrics

API latency
processing errors
queue depth
database availability

Workflow metrics

invoices successfully processed
invoices requiring manual correction
duplicate submissions
processing delays

Outcome metrics

invoices correctly finalized
time from submission to approval
correction costs
percentage of users completing the process without assistance

The three layers answer different questions.

Infrastructure
↓
Is the system operational?

Workflow
↓
Is the process functioning?

Outcome
↓
Is the user achieving the goal?

The key is to connect these layers instead of monitoring them separately.

The Question That Changes Everything

Before designing a dashboard, I think we should ask:

"What does success look like from the user's perspective?"

Not the developer's perspective.

Not the infrastructure team's perspective.

Not the product manager's perspective.

The user's.

If you're building a payment system, success isn't simply returning HTTP 200.

It's helping authorized customers complete payments correctly and safely.

If you're building an educational platform, success isn't simply counting videos played.

It's helping people learn.

If you're building an AI assistant, success isn't merely generating an answer.

It's helping someone accomplish a task reliably.

Once that outcome is clear, we can work backward.

What events would indicate success?

What events would indicate failure?

What should we measure?

Which measurements are merely proxies?

And what important failures might still remain invisible?

These questions are just as important as choosing a monitoring tool.

But Outcomes Are Harder to Measure

There is a reason engineering teams rely heavily on technical metrics.

They're relatively straightforward.

A server is available or it isn't.

A request takes 100 milliseconds or 500 milliseconds.

A test passes or fails.

Human outcomes are messier.

Did the user abandon checkout because of a bug?

Because the delivery price was too high?

Because they changed their mind?

Because the interface was confusing?

We cannot assume that every abandoned checkout represents a software failure.

A decline in conversion could also reflect pricing, seasonality, or changing customer demand.

That's why outcome metrics need interpretation.

Sometimes they require user research.

Sometimes controlled experiments.

Sometimes investigating individual journeys.

Sometimes the best question isn't:

"What does the dashboard say?"

But:

"What happened to the people represented by these numbers?"

A Better Way to Investigate Production Problems

Suppose a customer reports a failed transaction.

Instead of checking only whether the server was healthy, we can trace the journey:

User starts checkout
↓
Address validation
↓
Cart confirmation
↓
Payment authorization
↓
Order creation
↓
Confirmation delivered

Which stage failed?

Did the user receive a meaningful explanation?

Was the payment authorized but the order never created?

Was the order created but the confirmation never delivered?

Could the user safely retry?

These are workflow questions.

And they often reveal problems that infrastructure health metrics cannot.

This is one reason I believe systems thinking matters so much in software development.

The value of the application comes from the complete experience, not from individual components operating in isolation.

AI Makes the Measurement Problem More Urgent

AI-generated functionality introduces another challenge.

Traditional software often follows explicit logic.

AI systems can produce plausible outputs that are still incorrect.

A chatbot can confidently give the wrong answer.

A coding assistant can generate code that passes a superficial check but violates a requirement.

A document-processing system can extract the wrong number while returning a perfectly valid JSON object.

The request succeeds.

The output format is correct.

The result is wrong.

We need evaluation methods that go beyond asking whether the application ran successfully.

This may include reference-based tests, checking factual support, measuring task completion, and reviewing samples of real-world failures with appropriate privacy protections.

No single metric can capture the entire quality of a complex AI system.

The objective is to build enough evidence to make better decisions.

Maybe We Should Stop Celebrating Green So Quickly

I like dashboards.

I like monitoring.

I like measurable engineering.

But I'm becoming increasingly cautious about what a green status actually means.

Green should mean:

"The conditions we're monitoring are within acceptable limits."

It shouldn't automatically mean:

"Everything is fine."

Those are different statements.

A green dashboard is useful evidence.

It is not a complete description of reality.

And perhaps that's one of the most important lessons software engineers can learn as our systems become more complex.

We don't just need better software monitoring. We need better definitions of success.

The Real Question Isn't Whether Your System Works

It's whether your system works for the people it was built to serve.

A fast application that frustrates users isn't necessarily a good application.

A highly available service that produces unusable results isn't necessarily successful.

An AI model that responds to every request isn't necessarily helpful.

And a dashboard full of green indicators doesn't mean every important problem has been solved.

Sometimes the most dangerous failure is the one we never thought to measure.

So the next time every indicator is green, I think we should ask one more question:

"What could still be going wrong that this dashboard would never show us?"

That question might teach us more than another dozen monitoring widgets.

Have you ever worked on a system where all the technical metrics looked healthy, but users were still struggling? What did your dashboards miss?

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The Next Interesting AI App Might Not Have a Screen

Jaideep Parashar — Fri, 09 Oct 2026 03:03:13 +0000

For years, we've been building technology around screens.

Websites.

Mobile apps.

Dashboards.

Chatbots.

AI assistants.

Almost every new software idea seems to follow the same pattern:

Build an interface. Wait for a human to interact with it. Return a result.

But I've been thinking about something different.

What if some of the most interesting applications of AI don't need us to open an app at all?

What if software could understand what was happening around it?

What if it could respond to physical conditions?

What if it could work without an internet connection?

And what if the interface wasn't a screen, but the world itself?

I think that's where software development starts becoming particularly interesting.

We Have Spent Decades Teaching Humans to Use Computers

Think about how we interact with technology today.

You want to check the temperature.

You open an app.

You want to control your lights.

You open another app.

You want to monitor your energy consumption.

You open a dashboard.

You want to understand why a machine stopped working.

You check a monitoring system.

The common assumption is that humans must continually interact with software to obtain information or initiate actions.

But consider a different possibility.

A system that monitors temperature, humidity, energy consumption, and occupancy.

Instead of waiting for someone to open a dashboard, it identifies an unusual pattern and takes an appropriate, authorized action.

No application needs to be opened.

No prompt needs to be written.

The software simply performs its intended function.

Of course, humans may still need an interface for configuration, exceptions, and oversight.

But the interface is no longer the center of the experience.

The outcome is.

The Physical World Is a Very Different Programming Environment

A traditional web application works with relatively structured inputs.

A user clicks a button.

Submits a form.

Uploads a file.

Makes an API request.

Physical systems have a much messier relationship with data.

A sensor can malfunction.

A microphone can capture background noise.

A camera can be obstructed.

A device can lose connectivity.

A battery can run low.

A motor can fail.

Two identical devices can behave differently under different environmental conditions.

And unlike a software bug that produces an incorrect message, a physical-system failure may affect something outside the computer.

That changes how we approach engineering.

The physical world doesn't care whether your demo worked perfectly.

It cares whether the system behaves reliably under real conditions.

AI Is Only One Part of the System

Imagine a small device designed to monitor unusual sounds in an industrial environment.

Its job is to identify patterns that might indicate a machine needs inspection.

A simplified architecture might look like this:

Microphone / Sensor
↓
Signal Processing
↓
Local ML Model
↓
Confidence & Rule Checks
↓
Decision Logic
↓
Alert / Human Inspection
↓
Feedback & Monitoring

Notice something interesting?

The AI model is only one component.

We still need:

reliable sensors
useful signal processing
appropriate confidence thresholds
sensible decision rules
failure handling
logging
security
human oversight

A model might detect an unusual sound correctly.

But if the microphone is defective, the system can still fail.

The intelligence of the model doesn't eliminate the need for engineering.

This is closely related to an idea I explored in Stop Building AI Agents. Start Building AI Systems.

The complete system matters more than any individual intelligent component.

What Happens When the Internet Disappears?

This is one of my favorite questions to ask about connected technology.

Does the application still work when the internet doesn't?

For many applications, losing connectivity is inconvenient.

For some physical systems, it can be a fundamental design problem.

Imagine a smart agricultural device monitoring soil conditions in a remote location. Imagine an AI powered device for eldercare add helps get optimum care in the old age. To build DIY AI ElderCare Programme

If every reading must travel to a cloud server before the system can make a basic decision, connectivity becomes part of the critical path.

But what if the device could process data locally?

It could:

Read the sensor.
Validate the measurement.
Evaluate a predefined rule or local model.
Trigger a permitted action.
Store the result.
Synchronize with the cloud when connectivity returns.

The cloud remains useful for fleet management, updates, historical analysis, and heavier computation.

But the essential function no longer depends entirely on it.

This is one reason I find edge computing fascinating.

It changes where decisions can happen.

Not Everything Needs an AI Model

Here's where I want to challenge the excitement around intelligent devices.

Suppose we want a system to switch on a fan when a room becomes too hot.

Do we need an LLM?

A neural network?

An autonomous agent?

Probably not.

A simple rule might be enough:

def should_start_fan(temperature, threshold=30): return temperature > threshold

That is understandable, testable, and predictable.

For a real controller, we'd also want hysteresis, sensor validation, and safe behavior when readings are unavailable.

Now imagine the system must predict equipment deterioration using several changing signals.

A machine-learning model may become useful.

The important distinction is this:

Use AI when the problem requires capabilities that simpler techniques cannot adequately provide.

Otherwise, we're adding complexity because the technology is available.

Not because the problem requires it.

The Next Interface Might Be a Conversation

Not every screenless system needs to operate autonomously.

Some might use voice.

Some might use gestures.

Some might respond to environmental signals.

And some might combine several forms of interaction.

Imagine a technician working on a machine.

Instead of stopping to open a manual on a tablet, the technician could ask:

"What does this warning pattern mean?"

A suitable system might combine a spoken question with equipment documentation and live diagnostic readings.

It could provide an explanation without requiring the technician to navigate a traditional application.

That's a different approach to interface design.

But it introduces engineering challenges too.

What happens when speech recognition fails?

What happens in a noisy environment?

What if two people speak at once?

What if the system misunderstands a safety-critical instruction?

The answer cannot simply be:

"Use a better model."

We need reliable interaction design, confirmation mechanisms, access controls, and safe failure behavior.

Hardware Makes Software Engineers Think Differently

There is a particular discipline that physical systems force upon us.

You cannot assume unlimited compute.

You cannot assume continuous connectivity.

You cannot assume perfect inputs.

You cannot assume a user will always be present to correct an error.

You may have to work within strict limits on:

power consumption
memory
processing capacity
response time
network availability
physical size
maintenance requirements

These constraints are not necessarily disadvantages.

They force us to ask better questions.

Does this computation need to happen locally?

Can we reduce the model size?

Can the system operate with intermittent connectivity?

What happens if a sensor provides incorrect data?

What is the safest action when the system is uncertain?

These are the kinds of questions that make technology engineering interesting beyond the software interface.

The Real Opportunity Is in Combining Technologies

I don't believe the next wave of useful applications will come from AI alone.

It will come from combinations.

AI + Sensors

Systems that interpret environmental and operational data.

AI + Robotics

Machines that can perceive conditions and perform carefully controlled physical tasks.

AI + Edge Computing

Applications that process important information closer to where it is generated.

AI + IoT

Connected devices that move beyond collecting data to helping interpret and act on it.

AI + Traditional Software

Reliable workflows where probabilistic models are used only where they add value.

These combinations are more interesting to me than simply adding another chatbot interface to an existing product.

But they are also harder.

Because when technology touches the physical world, reliability, maintenance, usability, and safety become part of the product, not optional features.

Maybe We've Been Asking the Wrong Question

For years, software teams have often begun with:

"What app should we build?"

With AI, that sometimes becomes:

"What AI feature should we add?"

Or:

"What agent should we create?"

I think we should start with a different question:

"What useful outcome should happen, and what is the simplest reliable system that can make it happen?"

Sometimes the answer will be a website.

Sometimes it will be a mobile application.

Sometimes it will be an API.

Sometimes it will be a basic automation rule.

And sometimes it will be a small device quietly performing its job in the background.

This connects to another idea I've explored: Why I Think Workflows Matter More Than Agents.

Technology should fit the problem.

We shouldn't reshape every problem to fit whichever technology is currently popular.

The Most Interesting Software Might Be the Software We Barely Notice

When technology works well, something interesting happens.

We stop thinking about it.

A good thermostat doesn't require our constant attention.

A well-designed monitoring system doesn't need someone watching a dashboard every second.

A reliable automation system doesn't need to demonstrate how intelligent it is.

It simply works within the limits it was designed for.

That doesn't mean screens will disappear.

Or that every device will become intelligent.

Or that every physical process should be automated.

But I think it represents an important shift in how we imagine software.

For a long time, we built systems that waited for humans to tell them what to do.

Increasingly, we can build systems that observe conditions, process information, and provide useful assistance within carefully defined boundaries.

The challenge is no longer just making software intelligent.

It's making intelligence useful in the real world.

And perhaps the next genuinely interesting AI application won't be another website, chatbot, or dashboard.

Perhaps it will be a small device with a sensor, a modest processor, a carefully chosen model, and one very specific job.

No spectacular interface.

No unnecessary complexity.

Just a system solving a real problem.

What do you think? Are we entering an era where the most interesting software won't necessarily live on our screens?

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I Think We Confuse Clean Code With Good Code

Jaideep Parashar — Fri, 02 Oct 2026 13:50:20 +0000

I've seen beautifully organized code that was difficult to understand.

I've also seen ugly-looking code that solved a complicated problem surprisingly well.

That made me wonder:

Are we sometimes confusing clean code with good code?

We talk a lot about clean code.

Small functions.

Meaningful names.

Design patterns.

Abstractions.

DRY principles.

Consistent formatting.

Well-organized files.

All of these things matter.

But there is a problem.

You can follow every clean-code principle and still build something that nobody understands.

Because clean code and clear code are not necessarily the same thing.

Clean Code Can Still Hide the Problem

Imagine opening a project and finding this:

services/
├── adapters/
├── factories/
├── strategies/
├── repositories/
├── handlers/
├── managers/
├── providers/
└── utilities/

Everything looks organized.

Every class has a name.

Every responsibility appears to have its own abstraction.

The code reviewer might even say:

"This is nicely structured."

But then you try to answer a simple question:

"What happens when a user clicks this button?"

And suddenly you're jumping through eight files.

The code is clean.

The architecture is organized.

But the system is difficult to understand.

That's the distinction I think we often miss.

However, in my last post, "I Think AI Is Making Coding Easier and Learning Harder", I emphasised how AI is making coding easier.

Clear Code Answers "Why?"

Clean code often focuses on how the code is written.

Clear code also focuses on why the code exists.

Consider this:

if user.age >= 18: allow_access()

It's simple.

It's readable.

But imagine the actual business rule is:

Users can access the feature if they are 18 or older, have completed identity verification, and are not currently suspended.

Now we might write:

if ( user.age >= 18 and user.identity_verified and not user.suspended ): allow_access()

That's still relatively clear.

But six months later, someone changes the requirement.

Perhaps suspended users can now access the feature under certain conditions.

The code may remain syntactically clean.

What matters now is whether the reason behind the rule is visible.

That's where comments, documentation, tests, naming, and architecture become important.

Not to explain what the code does.

To explain why it does it.

Comments Were Never the Enemy

There is a popular idea in software development:

"Good code shouldn't need comments."

I understand where this comes from.

Bad comments are absolutely a problem.

This:

# Increment counter by 1 counter += 1

doesn't help anyone.

The code already tells us that.

But consider:

We intentionally use a 5-minute window here.
The payment provider can send duplicate webhook events during retry periods.

That's valuable.

The code cannot fully explain the reason.

The comment captures the decision.

And decisions are often more important than syntax.

This is why I don't think the goal should be:

"Write code that needs no comments."

The better goal is:

"Write code that doesn't need comments to explain what it does, but use comments when the why isn't obvious."

Abstraction Can Make Code Less Clear

Abstraction is one of the most powerful tools in software engineering.

It is also one of the easiest to overuse.

Suppose we have:

send_email(user)

That's easy to understand.

But imagine the implementation eventually becomes:

send_email()
↓
NotificationManager
↓
NotificationFactory
↓
EmailProviderResolver
↓
EmailStrategy
↓
TransportAdapter
↓
SMTPService

Every individual component might be perfectly designed.

But the original operation has disappeared beneath the architecture.

This is the abstraction paradox:

An abstraction is useful when it hides unnecessary complexity.

But too many abstractions can create a new kind of complexity:

understanding where the real work happens.

DRY Can Become Wet

One of the first principles many developers learn is:

Don't Repeat Yourself.

It's a good principle.

But I've also seen developers create complicated abstractions simply because two pieces of code looked similar.

The problem is that similarity doesn't always mean they have the same reason to exist.

Two functions can look almost identical today while representing completely different business concepts.

If we force them into one abstraction, we save a few lines of code but create a dependency between two things that may eventually evolve differently.

Now changing one behavior requires understanding the other.

We eliminated duplication.

And introduced coupling.

Sometimes a little duplication is cheaper than a lot of abstraction.

AI Makes This Problem More Interesting

This becomes particularly relevant in the age of AI-assisted coding.

AI is very good at producing code that looks professional.

It can create:

classes
interfaces
abstractions
helper functions
design patterns
error handling
documentation
tests

The resulting code can look remarkably polished.

But polished code isn't automatically good architecture.

AI doesn't always know which abstraction is worth having.

It may optimize for patterns it has seen repeatedly.

And because the generated code often looks sophisticated, developers may be less likely to question it.

This creates a new review problem.

We shouldn't only ask:

"Is this code clean?"

We should also ask:

"Did we make the system harder to understand than it needed to be?"

The Simplest Code Isn't Always the Best Code Either

I don't want to replace one extreme with another.

"Keep it simple" can become another dogma.

Sometimes complexity is necessary.

Distributed systems are complex.

Security systems are complex.

Financial software can be complex.

Operating systems are complex.

Large-scale applications are complex.

The goal isn't to eliminate complexity.

The goal is to make sure the complexity exists for a reason.

There is a huge difference between:

Necessary complexity

and

Accidental complexity

The first comes from the problem.

The second comes from how we chose to solve it.

Good engineering tries to minimize the second without pretending the first doesn't exist.

I Now Ask a Different Question During Code Review

When I look at code, I don't want to ask only:

"Is this clean?"

I want to ask:

Can I understand the main flow?

If I have to jump through ten files to understand a simple operation, something may be wrong.

Can I understand the important decisions?

Why does this condition exist?

Why is this timeout five seconds?

Why are we retrying three times?

Why are we using this database?

Can I change something safely?

If a small requirement change requires understanding the entire application, the architecture may be hiding too much.

Can a new developer understand it?

This is one of my favorite tests.

Not:

"Can the person eventually figure it out?"

But:

"Can they build the right mental model without needing the original author beside them?"

That's a much higher standard.

Code Is Communication

We often think of programming as communicating with computers.

That's only half the job.

We're also communicating with the next developer.

And sometimes that developer is us six months later.

The computer doesn't care whether the architecture is intuitive.

It executes instructions.

Humans have to maintain the system.

That's why readability matters.

But readability isn't simply about formatting.

It's about reducing the mental effort required to understand a system.

A beautifully formatted maze is still a maze.

The Best Code Might Be Boring

I increasingly like boring code.

A function that does one obvious thing.

A straightforward database query.

A small number of dependencies.

A simple API.

A clear data flow.

A comment explaining an unusual decision.

A test showing an important business rule.

Nothing clever.

Nothing impressive.

Just understandable.

That kind of code rarely gets celebrated.

Nobody posts:

"Look at this extremely boring function I wrote today."

But boring software can be extraordinarily valuable.

Because the people maintaining it don't have to fight it.

My New Definition of Good Code

If I had to simplify it, I'd say:

**Clean code is code that is well structured.

Clear code is code that is easy to understand.

Good code is code that solves the right problem with an appropriate amount of complexity.**

Those are three different things.

And sometimes they overlap beautifully.

But not always.

A codebase can be clean without being clear.

It can be clear without being particularly elegant.

And it can be technically imperfect while still being the right solution for the problem.

That's why I don't think software engineering should be a competition to produce the prettiest code.

The objective is to build software that people can understand, change, operate, and trust.

Maybe We Need Fewer Rules

Software engineering has accumulated an enormous number of principles.

SOLID.

DRY.

KISS.

YAGNI.

Design patterns.

Clean architecture.

Domain-driven design.

Microservices.

Event-driven architecture.

Functional programming.

Object-oriented programming.

All of these ideas can be useful.

But principles are tools.

They aren't laws of physics.

The moment a principle starts making the software harder to understand, we should be willing to question whether we're applying it correctly.

That's probably one of the most important engineering skills:

Knowing when a good rule has become a bad decision.

The Question I Want to Leave You With

The next time you review a piece of code, don't ask only:

"Is this clean?"

Ask:

"Can I understand why this exists?"

And then ask something even more important:

"Did we make this more complicated than the problem required?"

Because the best code isn't necessarily the code with the most elegant abstractions.

It isn't necessarily the code with the fewest lines.

And it certainly isn't the code that looks the most impressive.

Sometimes the best code is simply the code that makes the next developer say:

"Okay. I understand."

And perhaps that's a better definition of clean code than we usually give it.

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I Think AI Is Making Coding Easier and Learning Harder

Jaideep Parashar — Fri, 25 Sep 2026 03:57:07 +0000

I can write code faster today than I could a few years ago.

AI can explain an unfamiliar library.

It can generate a function.

It can suggest a fix.

It can write SQL.

It can help debug an error.

It can even explain the code it just generated.

From a productivity perspective, this is incredible.

But I've started thinking about a different question:

What happens to the learning process when the difficult part of coding becomes optional?

I don't think AI is making developers less capable.

But I do think it has created a new risk:

We can now become productive before we become good.

And those are not the same thing.

The Struggle Was Part of the Learning

Think about how we traditionally learned programming.

You wrote something.

It didn't work.

You read the error.

You searched for an explanation.

You tried something else.

It failed again.

You read documentation.

You changed the code.

Eventually, it worked.

That process was frustrating.

But something important was happening during those frustrating hours.

You were building a mental model.

You were learning why the system behaved the way it did.

You weren't simply learning the answer.

You were learning how to arrive at the answer.

AI changes that equation.

Today, you can paste the error into an AI assistant and get a possible explanation almost immediately.

That's useful.

But it also means you can skip several steps of the learning process.

And sometimes those skipped steps were the most valuable ones.

The Dangerous Part Isn't AI

I don't think the problem is AI itself.

The problem is how we use it.

There is a big difference between:

"Explain why this error is happening."

and:

"Fix this."

The first can teach you something.

The second can simply remove the obstacle.

Both can get you moving again.

But they don't necessarily produce the same learning.

This distinction becomes even more important when you're learning something new.

If I already understand Python and ask AI to help me optimize a piece of Python code, that's one thing.

If I'm learning Python and ask AI to write every function I don't understand, that's very different.

In the second case, I may finish the project without actually learning how I built it.

Productivity Can Hide the Learning Gap

This is where things get interesting.

Imagine two developers learning the same technology.

Developer A spends three hours struggling with a problem.

Developer B asks AI and solves it in ten minutes.

On the surface, Developer B has been more productive.

But what happens when the same problem appears next month?

Developer A may recognize the pattern.

Developer B may need to ask AI again.

Neither person necessarily did anything wrong.

They optimized for different things.

One optimized for completion.

The other accidentally optimized for understanding.

The problem is that modern development increasingly rewards completion.

And AI is exceptionally good at helping us complete things.

I Don't Want to Go Back to Coding Without AI

This is important.

I'm not arguing that developers should stop using AI.

That would be like saying developers should stop using debuggers because debugging used to be harder.

Tools are supposed to make us more capable.

I use AI because it genuinely makes many parts of development faster.

The goal isn't to preserve unnecessary struggle.

The goal is to preserve useful struggle.

That's a very different idea.

If I'm spending thirty minutes trying to remember a syntax detail that I could verify in ten seconds, I don't think that struggle is valuable.

But if I'm trying to understand why an architecture behaves the way it does, immediately asking AI for the answer may deprive me of an important learning opportunity.

The "Try First" Rule

I've started thinking about a simple rule:

Try first. Ask AI second.

Not always.

But often.

If I encounter a problem I believe I can solve, I give myself some time to reason about it before asking AI.

I might:

read the error
inspect the code
form a hypothesis
test the hypothesis
search the documentation
try a solution

Then I ask AI.

And something interesting happens.

The AI's answer becomes much more useful because I have a mental model of the problem.

I'm no longer asking:

"What should I do?"

I'm asking:

"Here's what I think is happening. Am I missing something?"

That is a much more powerful interaction.

AI Should Challenge Your Thinking

This is also changing how I think about prompts.

Instead of always asking AI to produce something, we can ask it to challenge us.

For example:

"Here is my approach. Find the weaknesses."

Or:

"Don't solve this yet. Give me three questions I should investigate."

Or:

"I think this is the cause of the bug. Try to disprove my hypothesis."

Or:

"Review my solution, but don't rewrite it. Tell me what I misunderstood."

These interactions keep the developer inside the reasoning loop.

AI becomes less like an answer machine and more like a thinking partner.

That's a much healthier relationship.

There Is a Difference Between Knowing and Accessing

This may become one of the biggest distinctions in AI-assisted development.

I don't need to memorize every Python method.

I don't need to remember every SQL syntax variation.

I don't need to know every framework API by heart.

AI and documentation can handle a lot of retrieval.

But I still need to understand:

What am I trying to do?

Why does this approach make sense?

What assumptions am I making?

What could go wrong?

How would I know if the result is wrong?**

Those are not simply information-retrieval problems.

They are reasoning problems.

And I don't think AI makes those skills less important.

I think it makes them more important.

The New Developer Skill Might Be Knowing When Not to Ask

There is an interesting irony here.

As AI becomes better at answering questions, the skill of asking good questions becomes more valuable.

But perhaps there is another skill:

Knowing when to answer the question yourself first.

If I ask AI everything immediately, I may become extremely efficient at retrieving answers.

But I may become less practiced at generating them.

That distinction matters.

A calculator didn't make mathematicians less intelligent.

But someone who never learns basic arithmetic because a calculator exists has a different problem.

The tool isn't the issue.

Dependency is.

AI Can Make You a Better Developer

The same technology that creates this risk can also solve it.

AI can explain unfamiliar code.

It can give you alternative approaches.

It can challenge your assumptions.

It can create exercises.

It can act as a tutor.

It can review your implementation.

It can ask you questions instead of giving you answers.

It can help you understand why something works.

Used this way, AI doesn't replace the learning process.

It can actually accelerate it.

The difference is whether we're using AI to avoid thinking or to improve our thinking.

Maybe We Need a New Development Loop

The old loop looked something like:

Problem
↓
Think
↓
Code
↓
Test
↓
Debug
↓
Learn

AI can turn it into:

Problem
↓
Think
↓
AI assists
↓
Code
↓
Test
↓
AI challenges
↓
Human evaluates
↓
Learn

I prefer the second model.

Not because AI does more.

Because the human still does the important thinking.

The Real Risk Isn't Becoming Lazy

People often ask:

"Has AI made developers lazy?"

I'm not sure that's the most useful question.

Developers have always looked for ways to avoid unnecessary work.

That's part of engineering.

The better question is:

"Are we automating away work that was actually teaching us something?"

That's much harder.

Because some effort is waste.

And some effort is education.

The challenge is learning the difference.

I Don't Want to Become Faster at Being Wrong

This is probably the simplest way I can describe my concern.

AI can dramatically increase development speed.

But speed is not the same as progress.

If I misunderstand the problem and AI helps me implement the wrong solution in ten minutes instead of ten hours, I haven't necessarily become a better developer.

I've simply become faster at being wrong.

That's why I still want to understand the fundamentals.

I still want to read documentation.

I still want to debug.

I still want to write code without assistance sometimes.

Not because I dislike AI.

Because I want to remain capable without it.

The Goal Isn't to Code Without AI

I don't think the future belongs to developers who refuse to use AI.

And I don't think it belongs to developers who let AI do everything.

The interesting middle ground is this:

Use AI for leverage, but keep ownership of your understanding.

Let AI handle repetitive work.

Let it explain unfamiliar concepts.

Let it review your code.

Let it challenge your assumptions.

Let it help you move faster.

But occasionally close the AI window and ask yourself:

"Do I actually understand what I just built?"

If the answer is yes, you're probably using the tool well.

If the answer is no, perhaps the tool moved faster than your understanding.

And that's something worth paying attention to.

Because in the AI era, I don't think our biggest challenge will be learning how to make machines do more.

It may be learning how to make sure we continue to think while they do it.

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I Asked AI to Review My Code. It Found Bugs I Didn't See.

Jaideep Parashar — Fri, 18 Sep 2026 10:36:28 +0000

For a long time, I used AI in software development for one primary purpose:

Write the code.

Give it a requirement.

Get some Python.

Ask for a function.

Get a function.

Describe an API.

Get an API.

It was fast. Sometimes impressively fast.

But recently, I've been using AI differently.

Instead of asking:

"Can you write this for me?"

I've started asking:

"Can you review what I wrote?"

And the difference surprised me.

AI wasn't just helping me write code faster.

It was helping me look at my own code differently.

The Problem With Asking AI to Write Everything

There is an obvious benefit to AI-generated code.

You can move from an idea to a working prototype extremely quickly.

But there's a downside.

When AI writes the code, I can become focused on whether the code works rather than whether the code is actually good.

It is very easy to accept:

It runs → Ship it.

But software engineering doesn't end when the program runs.

What happens with unexpected input?

What happens when the API fails?

What happens when the database is unavailable?

What happens when the data is empty?

What happens when another developer has to maintain it six months from now?

Those questions are much harder to answer by simply generating more code.

That's where code review becomes interesting.

So I Tried a Different Workflow

Instead of starting with AI, I started with myself.

I wrote the code.

Then I asked AI to review it.

My prompt was intentionally simple:

Review this code as a senior software engineer. Don't rewrite it immediately. First identify potential bugs, edge cases, security concerns, maintainability problems, and unnecessary complexity. Explain why each issue matters.

That last sentence matters.

I didn't want AI to immediately produce another 100 lines of code.

I wanted it to think about the code first.

It Found Things I Hadn't Considered

Some findings were obvious once pointed out.

Others were not.

For example, a function might work perfectly for normal input but behave incorrectly when it receives:

an empty list
a missing value
unexpected data
a duplicate record
a failed API response
a very large input

None of these necessarily appear during a quick manual test.

And that's the interesting part.

A second reviewer doesn't need to discover something magical.

Sometimes it simply needs to ask:

"What happens if this assumption isn't true?"

That question alone can expose a surprising number of problems.

But AI Didn't Always Get It Right

This is where things get interesting.

I don't treat AI's review as truth.

Sometimes it identifies a genuine issue.

Sometimes it identifies something that isn't actually a problem.

And sometimes it suggests a change that would make the code more complicated without providing meaningful value.

This is exactly why I don't think AI replaces code review.

It changes the review process.

The developer still has to decide:

Is this actually a problem?
How serious is it?
Is the suggested solution appropriate?
What trade-off does it introduce?

AI can point at something.

It cannot automatically decide whether changing it is worth the cost.

The Most Useful AI Review Isn't "Fix My Code"

I've noticed a subtle difference between two prompts.

Prompt 1

Fix this code.

Prompt 2

Review this code and tell me what I should worry about.

The first asks AI to become the programmer.

The second asks AI to become the reviewer.

I find the second approach much more interesting.

Because it keeps me involved in the reasoning.

Instead of outsourcing the entire task, I'm using AI to increase the number of perspectives looking at the problem.

I Wouldn't Use AI as the Only Reviewer

There are still things AI may not understand.

It might not know why a particular architectural decision was made.

It might not understand the business requirement.

It may not know which performance trade-off is acceptable.

It may flag code that looks unusual but is intentionally designed that way.

And it can confidently make incorrect claims.

So I think of AI code review as another layer.

Something like:

Developer writes code
↓
Automated tests
↓
AI review
↓
Human review
↓
Production
↓
Monitoring

Each layer answers a different question.

Tests ask:

Does the software behave correctly for the cases we've defined?

AI review asks:

What problems might we have overlooked?

Human review asks:

Does this implementation make sense in the real system?

Monitoring asks:

What happens after real users start using it?

No single layer is enough.

There's Another Benefit I Didn't Expect

AI reviewing my code has also changed how I write code.

When I know I'll eventually ask an AI to review something, I become more deliberate about my implementation.

I think more about:

naming
assumptions
edge cases
error handling
duplicated logic
unnecessary complexity

In other words, the reviewer starts influencing the author.

And that's a useful side effect.

The goal isn't simply to have AI catch mistakes.

The goal is to become better at anticipating mistakes before they happen.

This Applies Beyond AI-Generated Code

There's a temptation to think AI code review is useful only when AI generated the original code.

I don't think that's true.

Human-written code has bugs.

Human-written code has assumptions.

Human-written code becomes difficult to maintain.

Human-written code gets reviewed by people who are tired, rushed, or unfamiliar with the particular problem.

A second perspective can be valuable regardless of who wrote the original code.

AI is simply an unusually fast second perspective.

But Don't Confuse Review With Testing

This distinction is important.

AI can tell me:

"This function may fail when the API returns an empty response."

That's useful.

But a test can actually execute the scenario.

For example:

def test_empty_api_response(): response = process_api_response([]) assert response == expected_result

The two approaches complement each other.

AI can help identify what to test.

Tests can verify what actually happens.

That's a much stronger workflow than relying on either one independently.

I've written before about how developers routinely test traditional software while treating AI behavior differently. I think the same principle applies here:

AI should become part of an engineering discipline, not a replacement for it.

The Bigger Lesson

The most interesting change isn't that AI can review code.

We've known that AI can analyze code.

The bigger change is that we can now cheaply introduce additional reasoning into our development process.

A developer working alone can effectively have another pair of eyes available within seconds.

That doesn't mean the second pair of eyes is always correct.

It means the cost of asking for another perspective has become extremely low.

And when something is cheap enough, we can incorporate it into our workflow.

I'm Changing How I Use AI for Coding

I still use AI to generate code.

It would be silly not to take advantage of that capability.

But I don't want my relationship with AI to become:

Human thinks → AI writes.

I'd rather have:

Human thinks → AI helps build → AI challenges → Human evaluates → Tests verify.

That is a much healthier development loop.

Because the objective isn't to remove the developer from the process.

It is to make the developer better at the process.

Maybe AI's Best Coding Feature Isn't Code Generation

We spend an enormous amount of time talking about how quickly AI can write code.

I'm starting to think another capability deserves more attention:

AI can challenge the code we already wrote.

It can ask questions we didn't think to ask.

It can point toward assumptions we forgot we were making.

It can identify edge cases hiding behind apparently simple functions.

It can suggest tests.

It can act as a tireless second set of eyes.

But there is an important condition:

We still have to think.

If we blindly accept AI-generated code, we haven't improved the engineering process.

We've simply accelerated it.

And accelerated bad decisions are still bad decisions.

So perhaps the better question isn't:

"How can AI write more of my code?"

Maybe it's:

"How can AI help me become a better engineer?"

That's the question I'm increasingly interested in.

How are you using AI in your development workflow today, primarily as a code generator, debugger, reviewer, teacher, or something else?

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I Think Developers Are Building Too Much Software

Jaideep Parashar — Fri, 11 Sep 2026 03:36:19 +0000

There was a time when the difficult part of building software was writing the software.

Today, that is increasingly not true.

We have better frameworks, better libraries, better APIs, better cloud infrastructure, better developer tools, and now AI can generate a significant amount of the code for us.

Yet I keep seeing the same pattern:

We are getting better at building things, but not necessarily better at deciding what should be built.

And I think that is becoming one of the biggest engineering problems of our time.

Software Has Become Too Easy to Start

Think about what it takes to build an application today.

You can generate a frontend in minutes.

Connect an API.

Add authentication.

Add a database.

Deploy it.

Add an AI model.

Give the model access to tools.

Add an agent.

Add another service.

Add monitoring.

Add a queue.

Add a vector database.

Add an orchestration framework.

Before long, you have a surprisingly sophisticated architecture.

And sometimes you have solved a problem that didn't actually require any of it.

This is the paradox of modern software development:

The cheaper it becomes to build software, the easier it becomes to build unnecessary software.

AI Made This Problem Bigger

AI has changed the equation even further.

Previously, I might have spent several hours implementing a feature.

Today, I can describe the feature and have an AI coding assistant generate much of the implementation.

That is incredibly useful.

But it also removes an important friction.

When building something required significant effort, effort itself acted as a filter.

You naturally asked:

"Is this worth building?"

When implementation becomes cheap, that question becomes easier to ignore.

You can simply build it.

And then build another version.

And then add another feature.

And then add an agent because the agent framework looks interesting.

The cost of implementation falls.

But the cost of complexity does not disappear.

Every Feature Creates a Future Problem

A feature rarely costs only what it takes to build it.

It also creates:

code to maintain
bugs to investigate
dependencies to update
documentation to maintain
edge cases to handle
users to support
security concerns
performance considerations
monitoring requirements
decisions future developers need to understand

This is why a 30-minute feature can sometimes create a six-month maintenance problem.

The original implementation was cheap.

The system became expensive.

This is particularly dangerous with AI-generated code because the initial implementation can feel almost free.

You ask for a feature.

The code appears.

It works.

You move on.

Six months later, someone has to understand why seventeen components depend on it.

Complexity Is a Technical Debt You Can See Too Late

Developers are generally good at recognizing obvious technical debt.

Poor naming.

Duplicated code.

Missing tests.

Outdated dependencies.

Large functions.

But there is another type of technical debt that is harder to recognize:

unnecessary architecture.

You can have beautifully written code inside an unnecessarily complicated system.

You can have excellent abstractions that nobody needed.

You can have perfectly implemented microservices where a single application would have been enough.

You can have an impressive AI agent where a deterministic workflow would have been safer.

The individual components can all be technically correct.

The overall system can still be wrong.

I Am Increasingly Suspicious of "More"

More services.

More abstractions.

More tools.

More frameworks.

More automation.

More agents.

More integrations.

More dashboards.

More features.

More AI.

None of these things are inherently bad.

The problem starts when more becomes the default definition of progress.

Sometimes the best engineering decision is to remove something.

Remove a dependency.

Remove an abstraction.

Remove a service.

Remove an AI step.

Remove a feature.

Remove an entire workflow.

That can be harder than adding something.

Because adding demonstrates activity.

Removing requires judgment.

The Best Architecture Might Look Boring

There is something I have increasingly come to appreciate about good systems:

They can look surprisingly boring.

Imagine two solutions.

System A

User → API → Database → Response

System B

User → Gateway → Service A → Queue → Service B → Agent → Tool Router → Vector DB → External API → Validation Service → Event Bus → Service C → Database → Response

System B looks more sophisticated.

But sophistication isn't the same thing as quality.

If both systems solve the same problem reliably, System A may be dramatically better.

Not because it uses newer technology.

Because it has fewer things that can break.

This is why I increasingly believe that simplicity is not the absence of engineering.

It is the result of engineering judgment.

This Is Where AI Agents Fit Into the Conversation

This is also why I have become cautious about the current obsession with AI agents.

Agents are useful.

There are problems where dynamic decision-making, tool selection, iteration, and autonomy genuinely create value.

But if a deterministic workflow can solve the problem, an agent may simply introduce additional uncertainty.

The question shouldn't be:

"Can I turn this into an agent?"

The better question is:

"Does this problem actually require autonomous decision-making?"

That distinction matters.

I wrote about this in more detail in my article “Stop Building AI Agents. Start Building AI Systems.”

The broader principle is the same:

Don't optimize for technological sophistication. Optimize for the outcome.

We Need a New Engineering Question

For a long time, developers were encouraged to ask:

"How do I build this?"

Then we learned to ask:

"How do I build this well?"

I think there is another question we need to put before both:

"Should this exist at all?"

That question applies to much more than software.

Should this feature exist?

Should this service exist?

Should this automation exist?

Should this database exist?

Should this AI component exist?

Should this entire application exist?

Sometimes the answer will be yes.

But sometimes the answer will be:

No.

And that's not failure.

That's engineering.

AI Makes Judgment More Valuable, Not Less

This is perhaps the most interesting consequence of AI-assisted development.

If AI continues making implementation faster, then implementation itself becomes less differentiated.

The valuable skills move upward.

Understanding the problem.

Defining the right requirements.

Choosing the right architecture.

Recognizing unnecessary complexity.

Knowing when not to automate.

Knowing when not to use AI.

Understanding trade-offs.

Evaluating whether something actually works.

And, perhaps most importantly:

knowing what not to build.

AI can generate ten possible implementations before you've finished explaining the problem.

That makes judgment more important, not less.

Before You Build the Next Thing

I've started thinking about software decisions through a much simpler filter.

Before building something, ask:

What problem does this solve?

If you cannot explain the problem clearly, stop.

Who actually needs it?

"Someone might use this" is not a strong enough answer.

What is the simplest solution?

Not the most impressive.

The simplest.

What complexity will this introduce?

Think beyond today's implementation.

Does this need automation?

Maybe a manual process is perfectly acceptable.

Does this need AI?

Maybe a rule, query, function, or conventional application is better.

What happens if we don't build it?

This is the question I think we ask far too rarely.

Sometimes nothing happens.

And that may be the best outcome.

The Builder's Paradox

We are entering an unusual period in software development.

Building is becoming incredibly easy.

But deciding what deserves to be built may become increasingly difficult.

That creates a strange paradox:

The better our tools become, the more important restraint becomes.

When writing code was expensive, we naturally avoided unnecessary code.

When infrastructure was difficult, we naturally avoided unnecessary infrastructure.

When software development becomes increasingly assisted by AI, we will need a different constraint.

Judgment.

Not everything that can be built deserves to exist.

Not every workflow needs automation.

Not every application needs AI.

Not every AI application needs an agent.

And not every technical problem needs a technical solution.

Maybe the next great developer skill isn't building faster.

Maybe it's learning to look at a problem and confidently say:

"We don't need to build this."

What do you think?

Are developers becoming better at building software or simply better at building more of it?

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Stop Building AI Agents. Start Building AI Systems.

Jaideep Parashar — Fri, 04 Sep 2026 05:27:08 +0000

There's a phrase I keep seeing everywhere in AI development:

"We need an AI agent."

Need to analyze documents?

Build an agent.

Need to automate a workflow?

Build an agent.

Need to interact with APIs?

Build an agent.

Need to write code?

Build an agent.

At some point, I started asking a different question:

Why are we so obsessed with building agents when what users actually need are reliable systems?

I don't think the agent is the product.

The system is the product.

And that distinction matters.

An Agent Is Only One Component

Let's take a simple AI customer-support application.

The agent might be responsible for deciding which tool to call.

But the actual application needs much more:

User
↓
Authentication
↓
Request Validation
↓
Context Retrieval
↓
AI Agent
↓
Tool Selection
↓
API / Database
↓
Output Validation
↓
Safety Checks
↓
Response
↓
Logging
↓
Evaluation

Where is the agent?

Right in the middle.

It's important.

But it's not the entire system.

This is why I believe we're spending too much time discussing agent capabilities and not enough time discussing system architecture.

The Agent Is Not Your Architecture

I've seen AI projects where the architecture diagram looks like this:

User
↓
AI Agent
↓
Magic

It makes for a great presentation.

It doesn't make for a great production system.

Real applications need answers to much less glamorous questions:

Where does the data come from?
How is context retrieved?
What happens when retrieval fails?
What happens when the model produces invalid output?
How are tool calls validated?
How are errors handled?
How do we monitor the system?
How do we evaluate quality?
Who owns the system after deployment?

These questions aren't exciting.

They're engineering.

And engineering is what turns an AI demo into a product.

Start With the Workflow

Before I decide whether an application needs an agent, I first map the workflow.

For example:

Customer Question
↓
Identify Intent
↓
Retrieve Relevant Information
↓
Generate Response
↓
Validate Response
↓
Escalate if Necessary

Maybe that's all we need.

Why introduce an autonomous agent if the workflow is already predictable?

This is the central idea behind my article Why I Think Workflows Matter More Than Agents.

A workflow gives us control.

An agent gives us flexibility.

We should choose flexibility only when the problem actually requires it.

Agents Make Sense When Decisions Are Dynamic

I'm not anti-agent.

Quite the opposite.

Agents are extremely useful when the path through the system cannot be predetermined.

For example:

Research Request
↓
Agent
┌────┼────┐
↓ ↓ ↓
Web SQL GitHub
└────┼────┘
↓
Synthesize
↓
Validate

The agent can determine which tools are necessary.

That's valuable.

But notice something important.

The agent still operates inside a larger system.

That's the distinction I think we need to make.

Reliability Is a System Property

Suppose your agent is incredibly intelligent.

It can reason.

It can plan.

It can use tools.

It can recover from errors.

Does that guarantee that your application is reliable?

No.

Reliability also depends on:

Data quality
Retrieval quality
API reliability
Validation
Authentication
Observability
Evaluation
Infrastructure

You can have an excellent agent inside a terrible system.

The user will still experience a terrible product.

Evaluate the System, Not Just the Model

This is another area where AI development needs to mature.

Developers naturally evaluate the model:

"Which model gives better answers?"

But production systems need broader evaluation.

Consider:

Model Quality
+
Retrieval Quality
+
Tool Accuracy
+
Workflow Reliability
+
Latency
+
Cost
+

Safety

System Quality

I've written about this in Most Developers Test Their Code. Why Don't They Test Their AI?

The important shift is from:

"Is the model good?"

to:

"Does the complete system consistently achieve the desired outcome?"

That's a much more useful question.

For organizations, this can be formalized through an AI Evaluation Framework: 7 Critical Metrics for AI Success, which looks beyond model capability toward measurable AI performance.

Context Is Part of the System

Another common mistake is treating the prompt as the entire intelligence layer.

It isn't.

Consider a coding assistant.

The model might receive:

Prompt:
"Fix this bug."

But the system may also provide:

Repository
+
Relevant Files
+
Documentation
+
Dependencies
+
Previous Errors
+
Coding Standards

That context can completely change the quality of the result.

This is why I've argued that context engineering is becoming more important than simply optimizing prompts.

The model doesn't just need instructions.

It needs the right information.

MCP Makes Systems More Connected

This is where MCP becomes particularly interesting.

Instead of building a separate custom integration for every AI capability, MCP provides a standardized way for AI applications to interact with external tools and data.

For example:

AI System
↓
MCP
┌──┼──────┐
↓ ↓ ↓
GitHub DB Files

Now the agent or model isn't isolated.

It becomes part of a connected system.

I've explored practical examples in 5 MCP Servers That Changed How I Build AI Workflows.

Don't Confuse More Components With More Intelligence

There's another trap.

Once developers discover agents, they start adding components.

One agent becomes three.

Three become ten.

Then comes:

Agent supervisor
Planner agent
Research agent
Coding agent
Reviewer agent
Memory system
Vector database
Multiple models

Eventually:

Simple Problem
↓
Complex Architecture
↓
More Failure Points
↓
More Debugging
↓
More Cost

This is one reason I wrote The Hidden Cost of Using Too Many AI Tools.

Complexity isn't evidence of sophistication.

Sometimes it's evidence that we haven't simplified the problem enough.

The Production Test

Here's a test I like to use.

Before calling an AI application production-ready, ask:

Can we reproduce failures?

If not, observability is weak.

Can we measure quality?

If not, evaluation is weak.

Can we control the workflow?

If not, architecture may be too autonomous.

Can we replace the model?

If not, the system may be too tightly coupled.

Can another developer maintain it?

If not, complexity may already be too high.

These questions tell me much more than whether an agent can successfully complete a demo.

Build the System Around the Outcome

The most important shift is this:

Don't start with:

"What agent should we build?"

Start with:

"What outcome are we trying to reliably produce?"

Then work backward.

Desired Outcome
↓
Process
↓
Workflow
↓
Context
↓
Tools
↓
Model
↓
Agent — only if necessary
↓
Evaluation
↓
Production

The agent becomes a design decision.

Not the starting assumption.

My Rule for AI Architecture

I now think about AI systems using a simple hierarchy:

Outcome
↓
Process
↓
Workflow
↓
Context
↓
Tools
↓
Model
↓
Agent

Notice where the agent appears.

Near the bottom.

That's intentional.

Because an agent should exist to serve the architecture.

The architecture shouldn't exist to justify having an agent.

Final Thoughts

The AI industry has become very good at building autonomous demos.

Now I think we need to become much better at building dependable AI systems.

Systems that can:

Be evaluated
Be monitored
Be debugged
Be governed
Be maintained
Be scaled
Be improved

Agents will absolutely be part of that future.

But they won't be the whole future.

The winning AI products won't necessarily have the most agents.

They'll have the best-designed systems around those agents.

So before you start your next agent project, pause for a moment.

Draw the workflow.

Define the outcome.

Identify the context.

Determine the tools.

Design the evaluation.

And only then ask:

"Do I actually need an agent?"

That question alone can save you weeks of unnecessary engineering.

Your AI Demo Works. So Why Is Your Product Failing?

Jaideep Parashar — Fri, 28 Aug 2026 08:31:56 +0000

I've seen this pattern repeatedly in AI development.

Someone builds an impressive prototype.

The prompt works.

The demo looks amazing.

Everyone in the meeting gets excited.

Then the project enters the real world.

And suddenly everything becomes difficult.

The responses aren't consistent.

Users ask questions nobody anticipated.

The context is incomplete.

Costs increase.

Latency becomes a problem.

Nobody knows how to evaluate the system.

Eventually, the "successful AI project" becomes another abandoned prototype.

This leads to an uncomfortable question:

If the demo worked, why didn't the product?

I think the answer is simple.

A demo proves that something is possible. A product has to prove that it is reliable.

A Demo Is Not a Product

Consider an AI document assistant.

Your prototype might look like this:

PDF
↓
LLM
↓
Question
↓
Answer

You upload one excellent document.

Ask three carefully selected questions.

The model produces impressive answers.

Demo complete.

But production looks more like this:

User
↓
Authentication
↓
Document Retrieval
↓
Context Selection
↓
LLM
↓
Validation
↓
Logging
↓
Evaluation
↓
Response

Now you have an engineering system.

And every additional component introduces potential failure.

The Prototype Trap

AI makes prototyping incredibly easy.

That's both a blessing and a problem.

A developer can create something impressive in an afternoon.

That creates the illusion that the hard part is finished.

It isn't.

The prototype answers:

"Can we make this work?"

Production asks:

"Can we make this work reliably thousands of times?"

Those are completely different questions.

The First Problem: Nobody Defined "Good"

This is probably the biggest issue I see.

Teams build an AI application without defining what a good output actually means.

Imagine an AI coding assistant.

What does success mean?

Is the generated code:

Syntactically valid?
Functionally correct?
Secure?
Tested?
Consistent with the existing architecture?
Maintainable?

If you don't define these criteria, you can't properly evaluate the system.

And if you can't evaluate it, you can't improve it systematically.

I've written about this problem in Most Developers Test Their Code. Why Don't They Test Their AI?

The principle is straightforward:

AI systems need tests too.

The Second Problem: The Context Is Wrong

Another common mistake is blaming the model when the real problem is context.

Suppose you're asking an AI assistant to fix a Python bug.

You provide:

Fix this authentication bug.

But the system doesn't know:

Which framework you're using
Your database schema
Authentication architecture
Existing middleware
Coding conventions
Related files

Even a powerful model is operating with incomplete information.

That's why I've become increasingly interested in context engineering.

The quality of an AI system depends not only on the instruction but also on the information supplied around it.

I explored this in Why Context Engineering Is More Important Than Prompt Engineering.

The Third Problem: The Workflow Was Never Designed

Another common pattern is:

User
↓
LLM
↓
Answer

That's fine for a chatbot.

It isn't necessarily enough for a production application.

A reliable AI system might instead look like:

Request
↓
Classify
↓
Retrieve Context
↓
Generate
↓
Validate
↓
Apply Business Rules
↓
Return Result
↓
Log Outcome

The model is only one component.

This is also why I believe workflows matter more than agents.

Before adding autonomy, memory, planning, or multiple agents, I want to know whether the underlying workflow is actually well designed.

The Fourth Problem: Teams Add AI Before Fixing the Process

This one is particularly important in enterprise AI.

Imagine a company has a slow approval process.

Instead of examining the process, they immediately add an AI agent.

The agent now automates:

Bad Process
↓
AI
↓
Faster Bad Process

That's not transformation.

That's automation of inefficiency.

I've explored this extensively in Why You Should Fix Your Process Before Implementing AI.

The process should determine where AI belongs, not the other way around.

The Fifth Problem: Nobody Owns the System

Who owns the AI application after launch?

The developer?

The product team?

The data team?

The business?

Nobody?

This sounds like an organizational question, but it directly affects technical quality.

Someone needs to own:

Evaluation
Prompt changes
Model changes
Data quality
Monitoring
Security
Cost
User feedback

Without ownership, AI systems slowly degrade.

What I Now Ask Before Calling an AI Project "Ready"

Before moving an AI project from prototype to production, I want answers to five questions:

What problem are we solving?
What does a good output look like?
What context does the system need?
How will we evaluate failures?
Who owns the system after launch?

If we can't answer these questions, we're probably not ready.

We're still experimenting.

And that's completely fine.

Experimentation isn't failure.

Pretending an experiment is a production system is.

Build the Smallest Reliable System

My philosophy has changed considerably as I've built more AI systems.

I don't want the most sophisticated architecture.

I want the smallest architecture that reliably solves the problem.

That might mean:

Python
+
FastAPI
+
One LLM
+
A database
+
A simple workflow
+
Evaluation

Not:

7 agents
+
4 frameworks
+
3 vector databases
+
12 tools
+
complex orchestration

More technology doesn't automatically create more intelligence.

Sometimes it creates more places for things to break.

I've discussed this broader problem in The Hidden Cost of Using Too Many AI Tools.

Turn the Prototype Into an Engineering System

If I had to summarize the transition in one diagram, it would be:

DEMO

Prompt
↓
LLM
↓
Interesting Output

PRODUCT

Problem
↓
Workflow
↓
Context
↓
LLM
↓
Validation
↓
Evaluation
↓
Monitoring
↓
Continuous Improvement

That's the difference.

The model didn't necessarily change.

The engineering around the model did.

Final Thoughts

The AI industry has become exceptionally good at creating impressive demos.

I'm less interested in impressive demos.

I'm interested in systems that continue working after the demo ends.

Systems that can be tested.

Systems that can be monitored.

Systems that can be improved.

Systems that developers can actually maintain.

That's where I think the next level of AI engineering will come from.

Not from making the demo more impressive.

From making the system more dependable.

So the next time your AI prototype works perfectly, don't celebrate too early.

Ask the harder question:

"What happens when this meets 10,000 real users?"

That's when the real engineering begins.

Most Developers Test Their Code. Why Don't They Test Their AI?

Jaideep Parashar — Fri, 21 Aug 2026 05:21:43 +0000

Here's something I've been thinking about while building AI systems:

Developers are obsessed with testing code.

We write unit tests.

Integration tests.

End-to-end tests.

CI pipelines.

Code reviews.

Linting.

Type checking.

But then we build an AI feature and suddenly the testing strategy becomes:

"I tried it three times and it seems pretty good."

That's not testing.

That's optimism.

And I think this is becoming one of the biggest weaknesses in AI development.

AI Applications Are Software

Consider a simple AI coding assistant.

The workflow might look like this:

User Request
↓
Context Retrieval
↓
Prompt
↓
LLM
↓
Generated Code
↓
Validation

Every component can fail.

The retrieval can return the wrong files.

The context can be incomplete.

The prompt can be ambiguous.

The model can hallucinate.

The generated code can contain bugs.

Yet many AI applications have no automated way of detecting these failures.

We wouldn't accept that standard from a normal API.

Why should AI be different?

"It Worked Once" Means Almost Nothing

Suppose you're building a system that converts natural language into SQL.

You test:

"Show me the top 10 customers by revenue."

The model generates:

SELECT customer_name, SUM(revenue) AS total_revenue
FROM sales
GROUP BY customer_name
ORDER BY total_revenue DESC
LIMIT 10;

Looks good.

You ship it.

Then a user asks:

"Show me the top 10 customers by revenue in 2025,
excluding cancelled orders."

Suddenly your system may produce completely different behavior.

AI outputs are probabilistic.

That means testing one input isn't enough.

Build an Evaluation Dataset

One of the simplest things an AI builder can do is create a small evaluation dataset.

For example:

test_cases = [
    {
        "input": "Find the top 10 customers by revenue.",
        "expected_contains": ["GROUP BY", "ORDER BY", "LIMIT"]
    },
    {
        "input": "Find revenue for 2025 excluding cancelled orders.",
        "expected_contains": ["2025", "cancelled"]
    },
]

Now you can run your AI system against the same cases whenever you change:

The prompt
The model
The retrieval system
The context
The workflow

That changes everything.

You're no longer asking:

"Does this feel better?"

You're asking:

"Did performance improve?"

Prompts Need Tests Too

This is one reason I don't think prompt engineering is disappearing.

A production prompt isn't just something you write once.

It is part of the system.

And if you change it, you should know whether the change improved the output.

I discussed the importance of this broader discipline in The Real Reason Prompt Engineering Isn't Going Away.

The next step is to connect prompt engineering with evaluation.

Think of it like software:

Prompt v1
↓
Evaluation
↓
Results
↓
Prompt v2
↓
Evaluation
↓
Compare

That's much more reliable than changing prompts based on intuition.

Context Needs Testing Too

Here's another problem.

You can have a perfect prompt and still get a terrible answer because the AI received the wrong context.

Imagine a coding assistant receives:

Prompt:
"Fix the authentication bug."

Context:

5 unrelated files
+
outdated documentation
+
wrong configuration

The model may generate perfectly reasonable code based on completely incorrect information.

This is why I believe context engineering is becoming just as important as prompt engineering.

I wrote about this in Why Context Engineering Is More Important Than Prompt Engineering.

The lesson is simple:

Don't only test what you ask the model. Test what you give the model.

Workflows Need Evaluation

This becomes even more important when AI is part of a larger workflow.

Consider:

User
↓
Retriever
↓
LLM
↓
Tool Call
↓
Validation
↓
Final Response

Where did the failure happen?

You need to know.

Was the retrieval wrong?

Did the model select the wrong tool?

Did the API return bad data?

Did validation fail?

This is one reason I've argued that workflows often matter more than agents.

A well-defined workflow gives you clear places to measure and debug.

I explored that argument in Why I Think Workflows Matter More Than Agents.

Start Small

You don't need an expensive AI evaluation platform to begin.

Start with 20–50 representative test cases.

For each case, record:

Input
Expected Behavior
Actual Output
Pass/Fail
Notes

Then run the dataset whenever you make a significant change.

Over time, your evaluation dataset becomes one of the most valuable assets in your AI project.

It captures what "good" actually means.

My AI Evaluation Rule

I've started thinking about AI systems in three layers:

Can it produce an answer?

Basic functionality.

Can it produce a correct answer consistently?

Reliability.

Can we measure whether it is improving?

Engineering maturity.

The third question is where many AI projects struggle.

If you cannot measure improvement, you're mostly guessing.

Evaluation Is the Missing Layer

The AI industry has spent enormous effort improving:

Models
Prompts
Agents
RAG
MCP
Vector databases

But evaluation deserves the same attention.

Because eventually every AI system needs to answer one uncomfortable question:

"How do you know it works?"

Not:

"The demo looked impressive."

Not:

"The model is highly capable."

Not:

"Users seem to like it."

Show me the evaluation.

That's the engineering mindset I want to see more often in AI.

Build This Into Your GitHub Workflow

I'm also adding a simple AI evaluation starter to my companion AI Builder resources.

A useful structure is:

ai-evaluation/
├── README.md
├── test_cases.json
├── evaluate.py
├── results.csv
└── prompts/
├── v1.txt
└── v2.txt

The idea is straightforward:

Prompt changes → Run tests → Record results → Compare versions.

This turns experimentation into an engineering process.

Final Thoughts

I don't think AI development should be:

Prompt
↓
Looks good
↓
Ship

It should look more like:

Build
↓
Evaluate
↓
Measure
↓
Improve
↓
Evaluate Again
↓
Deploy

That's how we build reliable software.

And I believe that's how we need to start building reliable AI.

The future of AI engineering won't belong only to people who know how to make models produce impressive outputs.

It will belong to developers who can measure, reproduce, debug, and improve those outputs.

Because the most important question in AI isn't:

"Can the model do it?"

It's:

"Can I prove that my system does it reliably?"

The 7 AI Repositories I Starred This Month

Jaideep Parashar — Mon, 17 Aug 2026 06:31:54 +0000

I don't star GitHub repositories just because they are popular.

A repository earns a star from me when I can see myself returning to it later.

Maybe it solves a real engineering problem.

Maybe it introduces a new architecture.

Maybe the code teaches me something.

Or maybe it represents where AI development is heading.

I've been spending a lot of time exploring AI repositories around agents, workflows, RAG, MCP, browser automation, model training, and API development.

These are seven repositories that stood out to me recently.

Not because you need all seven.

But because each one represents an important direction in AI development.

1. OpenAI Cookbook

Repository: https://github.com/openai/openai-cookbook

If you're building with the OpenAI API, this is one repository I would keep bookmarked.

The OpenAI Cookbook contains practical examples and guides covering common API development tasks, with many examples written in Python.

What I particularly like is the implementation-first approach.

Instead of spending hours reading theoretical explanations, you can study working examples and adapt them to your own application.

It's useful for:

API integration
Structured outputs
Embeddings
Agents
Evaluations
Multimodal applications

For beginners, it can also serve as a bridge between understanding an AI concept and actually implementing it.

2. LangChain

Repository: https://github.com/langchain-ai/langchain

LangChain remains one of the most important repositories in the LLM application ecosystem.

But I don't recommend it simply because it is popular.

I recommend understanding it because it exposes you to the building blocks behind modern AI applications.

Models.

Tools.

Retrievers.

Agents.

Integrations.

Structured outputs.

If you're serious about AI engineering, studying how these components fit together is valuable even if you eventually choose another framework.

3. LangGraph

Repository: https://github.com/langchain-ai/langgraph

This is probably one of the repositories I would recommend most strongly to developers interested in agentic systems.

LangGraph focuses on building stateful, controllable agents and long-running workflows.

What interests me most isn't the word "agent."

It's control.

AI systems become difficult when you don't know what happened during execution.

LangGraph provides concepts around state, persistence, human-in-the-loop interaction, and workflow orchestration.

That aligns closely with an idea I've written about before:

I believe workflows often matter more than agents.

I explored that argument in Why I Think Workflows Matter More Than Agents:

https://dev.to/jaideepparashar/why-i-think-workflows-matter-more-than-agents-3p82

4. LlamaIndex

Repository: https://github.com/run-llama/llama_index

If you're interested in RAG, document intelligence, or connecting LLMs to private data, LlamaIndex deserves your attention.

Its ecosystem provides tools for ingesting and structuring information from sources such as:

PDFs
APIs
Documents
SQL databases
Knowledge bases

The important idea here is simple:

An LLM becomes much more useful when it can work with your data.

This is also why I've become increasingly interested in context engineering rather than thinking only about prompt engineering.

The quality of the information supplied to the model can matter just as much as the wording of the instruction.

5. Browser Use

Repository: https://github.com/browser-use/browser-use

This is one of the more interesting repositories if you're thinking about AI agents interacting with the actual web.

Browser Use allows AI agents to control browsers and perform actions such as navigating websites, clicking elements, entering information, and extracting data.

That opens up a different category of AI applications.

Instead of:

User → Prompt → LLM → Text

You can build:

User
↓
AI Agent
↓
Browser
↓
Website
↓
Action
↓
Result

That's a significant shift.

The AI isn't merely generating an answer.

It's interacting with software.

6. Model Context Protocol Servers

Repository: https://github.com/modelcontextprotocol/servers

MCP has become one of the most interesting developments in the AI tooling ecosystem.

The official MCP servers repository contains reference implementations that demonstrate how AI applications can interact with external tools and data sources.

The important concept isn't any individual server.

It's the standardization.

AI systems can connect to external capabilities through a common protocol rather than requiring a completely different integration approach every time.

I've explored this topic in detail in 5 MCP Servers That Changed How I Build AI Workflows:

https://dev.to/jaideepparashar/5-mcp-servers-that-changed-how-i-build-ai-workflows-16j6

If you're building AI workflows in 2026, I think MCP is worth understanding.

7. Unsloth

Repository: https://github.com/unslothai/unsloth

The last repository on my list takes us in a different direction.

Unsloth focuses on running and training AI models locally, including workflows around fine-tuning and open models.

This matters because not every AI builder wants to depend entirely on hosted APIs.

There are legitimate reasons to explore local models:

Privacy
Cost control
Experimentation
Customization
Offline development
Model fine-tuning

The open-model ecosystem is becoming increasingly important, and projects like Unsloth make experimentation considerably more accessible.

What I Look For Before Starring a Repository

I don't use GitHub stars as a popularity contest.

When I evaluate an AI repository, I usually ask:

Does it solve a real problem?
Is the architecture worth studying?
Is the project actively maintained?
Can I learn something from the code?
Could I actually use it in a project?

If the answer is yes to several of these questions, the repository gets my attention.

That's also why I don't believe you need hundreds of AI repositories in your bookmarks.

You need the right ones.

Don't Build Your AI Stack From GitHub Stars

There is a trap here.

You discover an impressive repository.

Then another.

Before you know it, you're running ten frameworks, six databases, four agent libraries, and three orchestration systems.

That's not an AI stack.

That's dependency collection.

I've written about this problem in The Hidden Cost of Using Too Many AI Tools:

https://dev.to/jaideepparashar/the-hidden-cost-of-using-too-many-ai-tools-poo

The objective isn't to use more technology.

It's to build better systems.

Final Thoughts

What I like about GitHub is that it gives us something AI hype often doesn't:

the opportunity to inspect the work.

You can read the code.

Study the architecture.

Look at the issues.

Examine pull requests.

Run the examples.

And decide for yourself whether something is actually useful.

That's how I prefer to learn AI.

Not just by reading what people say about a technology.

By opening the repository and seeing what developers are actually building.

These seven repositories are a good starting point.

But don't just star them.

Clone one. Read the code. Build something. Break it. Fix it.

That's where the real learning begins.

About the Author

Jaideep Parashar is the Founder & Director of ReThynk AI Innovation and Research Pvt. Ltd., Six Sigma Black Belt, Lean Expert, AI Strategist, researcher, author, and keynote speaker. Through Agentic Process Excellence™, he helps organizations combine Artificial Intelligence, Lean Six Sigma, and systems thinking to build reliable, scalable, and continuously improving business operations.

Why Context Engineering Is More Important Than Prompt Engineering

Jaideep Parashar — Fri, 07 Aug 2026 05:20:40 +0000

For the past few years, prompt engineering has been one of the hottest topics in AI.

Countless tutorials have promised the "perfect prompt."

Courses have been built around writing better prompts.

Developers have spent hours refining wording to squeeze better responses from large language models.

But after building AI systems across multiple projects, I think the conversation is changing.

Prompt engineering is still important.

However, I believe context engineering is becoming even more important.

The difference may sound subtle.

In practice, it changes how we design AI applications.

Prompts Tell AI What to Do

A prompt is simply an instruction.

For example:

Generate a FastAPI CRUD application.

That's a prompt.

A better version might be:

Generate a FastAPI CRUD application.

Requirements:

Python 3.12
SQLAlchemy
Pydantic v2
JWT Authentication
Unit Tests
Docker Support

The second prompt is more specific.

It reduces ambiguity.

That's exactly why prompt engineering became so valuable.

In fact, I previously wrote The Real Reason Prompt Engineering Isn't Going Away, where I explain why better prompts continue to improve AI systems even as language models become more capable.

But modern AI applications require much more than better prompts.

Context Gives AI the Information It Needs

Imagine asking an AI assistant:

Summarize our API documentation.

Without context, the model has no documentation.

Now imagine the workflow:

User Request
↓
API Documentation
↓
GitHub Repository
↓
Coding Standards
↓
Previous Conversations
↓
LLM
↓
Response

Nothing about the prompt changed dramatically.

What changed was the context.

And that often has a much greater impact on output quality.

Modern AI Applications Run on Context

Today's AI systems rarely depend on prompts alone.

Instead, they combine information from multiple sources:

Retrieved documents
Vector databases
APIs
Tool outputs
Conversation history
User preferences
System instructions
Business rules

The model reasons over all of this information before producing a response.

That's context engineering.

Think Like a Software Engineer

Developers rarely write software by placing all their logic inside a single function.

Instead, they organize:

Modules
APIs
Databases
Configuration
Services

AI systems benefit from the same thinking.

Instead of creating one enormous prompt, we should design systems that assemble the right context automatically.

The prompt becomes only one component of a larger architecture.

Retrieval Is Context Engineering

Retrieval-Augmented Generation (RAG) is a perfect example.

Without retrieval:

Question
↓
LLM
↓
Answer

With retrieval:

Question
↓
Vector Database
↓
Relevant Documents
↓
LLM
↓
Answer

The improvement doesn't come from rewriting the prompt.

It comes from supplying better information.

MCP Is Expanding the Idea Even Further

Model Context Protocol (MCP) pushes this concept beyond documents.

Instead of retrieving only stored knowledge, AI systems can access:

GitHub repositories
Databases
File systems
APIs
Development tools

That dramatically expands the available context.

If you're exploring this direction, I recommend reading 5 MCP Servers That Changed How I Build AI Workflows, where I share the MCP servers that have had the biggest impact on my own projects.

Organized Context Beats Random Conversations

One mistake I see frequently is treating AI chats as permanent knowledge storage.

Useful prompts disappear.

Examples are lost.

Architecture decisions become impossible to find.

That's why I maintain structured prompt libraries with documentation, categories, and version history.

The goal isn't simply to organise prompts.

It's to organise reusable context.

I explained my complete workflow in How I Organize 10,000+ Prompts Across Projects.

Context Makes Workflows More Reliable

One reason I emphasize workflows is that they naturally manage context.

A good workflow knows:

What information to retrieve
Which tools to call
What data to validate
What output to generate

The AI model isn't making random decisions.

It's operating inside a structured system.

That's one reason I believe workflows often create more reliable AI applications than jumping directly to autonomous agents.

I discussed this idea further in Why I Think Workflows Matter More Than Agents.

Businesses Need Context Too

This idea extends beyond software engineering.

Organizations often invest in AI before understanding their own processes.

Without operational context, AI has little foundation to build upon.

Business rules.

Approval flows.

Compliance requirements.

Documentation.

These are all forms of organizational context.

Before implementing AI at scale, it's worth assessing whether that context already exists.

I explored this in AI Process Assessment: 9 Signs Your Business Is Ready for AI.

My Perspective

I don't think prompt engineering is disappearing.

Far from it.

Prompt engineering remains one of the foundational skills for working with large language models.

But I think the industry's focus is expanding.

The question is no longer:

"How do I write a better prompt?"

It's becoming:

"How do I provide the right information at the right time?"

That's a much bigger challenge.

And it's also a much more interesting engineering problem.

Final Thoughts

The future of AI won't be determined by who writes the cleverest prompts.

It will be shaped by those who design systems that deliver the right context consistently.

Great prompts still matter.

But great context transforms AI from an impressive chatbot into a dependable software system.

As builders, our goal shouldn't be to create longer prompts.

It should be to create smarter systems that know what information an AI model needs, and when it needs it.

That's why I believe context engineering is becoming one of the most valuable skills in modern AI development.

About the Author:

Jaideep Parashar is the Founder & Director of ReThynk AI Innovation and Research Pvt. Ltd., AI Strategist, researcher, author, Six Sigma Black Belt, and Lean Expert. He writes about practical AI implementation, Agentic Process Excellence™, and building reliable AI systems that combine technical innovation with operational excellence.

Website: ReThynk AI

References:

Why I Think Workflows Matter More Than Agents

Jaideep Parashar — Fri, 31 Jul 2026 05:36:16 +0000

If you've spent any time in the AI community recently, you've probably noticed one trend.

Everything is becoming an AI agent.

Need to summarize documents?

"Build an agent."

Need customer support?

"Use multiple agents."

Need a coding assistant?

"Deploy an autonomous agent with memory, tools, planning, and self-reflection."

Agents are exciting, and they absolutely have their place.

But after building AI applications and experimenting with different architectures, I've reached a different conclusion.

I think workflows matter more than agents.

That doesn't mean agents are bad.

It means I believe too many builders start by asking, "How can I build an agent?" instead of asking, "What's the simplest workflow that solves this problem?"

That single change in thinking has improved the way I design AI systems.

Workflows Solve Problems. Agents Execute Them.

Imagine you're building an AI-powered support system.

A workflow might look like this:

User Question
↓
Retrieve Knowledge
↓
Generate Response
↓
Validate Output
↓
Send Reply

Everything is predictable.

Each step has a clear purpose.

Now compare that with a system where an autonomous agent decides:

Which tools to use
Which documents to search
Whether to call another agent
Whether to retry
Whether to re-plan

Sometimes that's exactly what's needed.

But sometimes you've introduced far more complexity than the problem requires.

Complexity Has a Cost

Every additional layer in an AI system introduces new challenges.

More prompts
More APIs
More debugging
More monitoring
More failure points

A workflow with five deterministic steps is often easier to understand than an autonomous system making dozens of decisions behind the scenes.

That's one reason I previously wrote Why I Think Most AI Agents Are Overengineered, where I discuss how many projects adopt autonomous architectures long before they've demonstrated the need for them.

For many applications, simplicity isn't a limitation.

It's an advantage.

Predictability Beats Cleverness

One of the biggest benefits of workflows is predictability.

When something goes wrong, you know exactly where to look.

Was retrieval incorrect?
Did the prompt fail?
Did the API return an error?
Did validation reject the response?

Each stage can be tested independently.

With highly autonomous agents, tracing failures often becomes much harder because decision-making is distributed across multiple planning steps.

As systems grow, observability becomes just as important as intelligence.

Good Workflows Scale Better

I've learned that scaling isn't only about handling more users.

It's also about maintaining systems over time.

A clear workflow makes it easier to:

Add new features
Replace models
Update prompts
Improve evaluation
Introduce governance

Because each component has a defined responsibility.

That's much harder when responsibilities are spread across multiple autonomous agents.

Integration Matters More Than Autonomy

One lesson that has consistently shaped my AI stack is this:

Connected systems create more value than isolated intelligence.

A workflow that integrates GitHub, APIs, databases, and language models often delivers more business value than a sophisticated agent working in isolation.

That's one reason I've invested time in Model Context Protocol (MCP). Standardized integrations reduce friction and allow workflows to interact with external systems in a reliable way.

If you're exploring this approach, 5 MCP Servers That Changed How I Build AI Workflows covers the MCP servers that have had the biggest impact on my own projects.

The Best AI Stack Supports Workflows

When I look at my own development environment, I don't think about individual tools.

I think about how they work together.

ChatGPT.
Cursor.
FastAPI.
GitHub.
MCP.
Python.

Each tool has one clear responsibility within a larger workflow.

I described this philosophy in My Personal AI Stack in 2026, where I explain why I choose tools based on integration rather than popularity.

The stack exists to support the workflow—not the other way around.

Businesses Don't Need Agents First

One mistake I see organizations make is assuming that AI success starts with deploying agents.

In reality, it usually starts much earlier.

With understanding the process.

If the underlying workflow is inefficient, autonomous agents often automate inefficiency instead of eliminating it.

That's why I believe organizations should evaluate their operational readiness before investing heavily in advanced AI architectures.

I explored this in AI Process Assessment: 9 Signs Your Business Is Ready for AI, which provides practical indicators for assessing whether a business is prepared for AI adoption.

Similarly, Why You Should Fix Your Process Before Implementing AI explains why improving the process itself often delivers greater returns than introducing new AI technologies.

When Do Agents Make Sense?

This isn't an argument against agents.

There are many scenarios where they're the right choice.

For example:

Multi-step research
Dynamic task planning
Software engineering assistants
Long-running automation
Complex orchestration across multiple tools

But I treat agents as an optimization.

Not a starting point.

I first ask:

Can a workflow solve this?
Can it remain deterministic?
Can it be monitored easily?
Can another developer maintain it?

Only when the answer becomes "no" do I consider adding autonomous behavior.

Final Thoughts

AI agents will continue to improve.

Planning will become better.

Reasoning will become stronger.

Frameworks will become easier to use.

But I don't think the future belongs to the most autonomous systems.

I think it belongs to the teams that design the best workflows.

Because workflows create clarity.

Clarity creates reliability.

And reliable systems create real business value.

Before building another agent, it may be worth asking a simpler question:

Could a well-designed workflow solve this just as effectively?

About the Author:

Website: ReThynk AI

References: