DEV Community: Sourav Kasula

Why AI Agents Need Memory (And Why This Might Be the Biggest Missing Piece in Today's AI)

Sourav Kasula — Wed, 03 Jun 2026 20:38:59 +0000

Most people think AI gets smarter when models get bigger.

Bigger model.
More parameters.
More GPUs.

But after reading how LinkedIn built its Cognitive Memory Agent (CMA), I realized something interesting:

The future of AI might not be about making models smarter.

It might be about helping them remember.

Imagine Meeting Someone Every Day...

Let's say you meet a coworker every morning.

Every single day.

But every morning they forget:

your name
your role
what project you're working on
every conversation you've ever had

Day 1:

"Hi, I'm Spike."

Day 2:

"Hi, I'm Spike."

Day 100:

"Hi, I'm Spike."

Sounds exhausting, right?

Ironically, that's exactly how many AI systems work today.

Every conversation starts from scratch.

The model may be intelligent, but it has no lasting memory.

It's like talking to someone with permanent short-term memory loss.

Why Context Windows Are Not Memory

A common misunderstanding is:

"ChatGPT remembers because it can see previous messages."

Not exactly.

That's context.

Not memory.

Think of context as a sticky note.

Think of memory as a notebook.

A sticky note helps during the current conversation.

A notebook helps across months and years.

Once the context window fills up, older information disappears.

True memory survives beyond the current interaction.

That's where things become interesting.

Human Memory vs AI Memory

Humans don't store information in one giant database.

We use different types of memory.

For example:

Conversational Memory

"What were we talking about five minutes ago?"

Episodic Memory

"Last year I worked on a difficult production issue."

Semantic Memory

"Java is an object-oriented language."

Procedural Memory

"I know how to ride a bicycle."

LinkedIn's Cognitive Memory Agent follows a surprisingly similar idea.

Instead of one memory store, it uses multiple memory layers.

Each layer remembers different things.

The Four Types of AI Memory

1. Conversational Memory

This is the easiest one to understand.

It remembers recent conversations.

For example:

You tell an AI recruiter:

"I'm hiring a Senior Java Engineer."

A few minutes later:

"Find candidates for that role."

The agent remembers what "that role" means.

Without conversational memory, the AI would ask you again.

Every single time.

2. Episodic Memory

This is memory of events.

Think of it as an AI diary.

Example:

A recruiter rejects five candidates because they lack Kubernetes experience.

The AI remembers this event.

Later it learns:

"Ah, Kubernetes seems important for this recruiter."

This isn't a permanent preference yet.

It's simply recording what happened.

Just like humans remember experiences.

3. Semantic Memory

This is where patterns emerge.

Instead of remembering individual events, the AI learns facts.

For example:

After observing dozens of hiring projects, it learns:

This team doesn't sponsor visas
This department prefers hybrid work
This recruiter likes concise candidate summaries

These become long-term knowledge.

The agent no longer needs to rediscover them every time.

4. Procedural Memory

This one is fascinating.

Procedural memory remembers how someone works.

Not what they know.

How they operate.

Imagine two recruiters.

Recruiter A:

Filters by experience
Then filters by skills
Then sends outreach

Recruiter B:

Accepts suggested candidates
Focuses heavily on message templates

Both achieve the same goal.

But their workflows are different.

The AI learns those workflows.

Over time it starts behaving more like its user.

That's personalization at a much deeper level.

The Real Problem Nobody Talks About

Memory sounds great.

But it introduces a new challenge.

What if the memory is wrong?

Imagine an AI remembers:

"This recruiter doesn't hire remote workers."

Six months later the company changes its policy.

Now the memory is outdated.

The AI starts making bad recommendations.

Humans have this problem too.

We call it outdated assumptions.

AI systems need mechanisms for:

forgetting
updating
conflict resolution
prioritizing recent information

Ironically, teaching AI what to forget may become just as important as teaching it what to remember.

Why This Matters for Enterprise AI

Most enterprise AI projects today focus on:

prompts
models
RAG
vector databases

Those are important.

But memory may become the next major differentiator.

Because eventually every company will have access to powerful models.

The question becomes:

Which AI actually understands its users?

The winner won't necessarily be the model with the highest benchmark score.

It may be the agent that remembers the right things at the right time.

My Take

The most interesting thing about LinkedIn's architecture wasn't the model.

It was the memory system.

The model provides reasoning.

The memory provides continuity.

Together they create something much closer to how humans work.

When people talk about the future of AI agents, they usually focus on intelligence.

I think memory deserves just as much attention.

Because an AI that remembers nothing can only react.

An AI that remembers well can adapt.

And adaptation is where truly useful agents begin.

What do you think?

Will future AI systems be defined more by their reasoning ability or by their memory?

Why I Think Backend Engineers Should Start Paying Attention to Generative AI..

Sourav Kasula — Wed, 20 May 2026 13:03:00 +0000

Notes from your fellow Engineer..

A few months ago, I was treating Generative AI the same way many backend engineers probably still are.

Interesting technology? Definitely.

Worth exploring at some point? Sure.

But directly relevant to backend engineering?

I wasn’t fully convinced yet.

Most of my day-to-day work still revolved around things like:

APIs
microservices
distributed systems
cloud infrastructure
debugging strange production issues
scalability problems
Kubernetes deployments

AI felt like a separate world.

But lately, I’ve started noticing something interesting.

AI is slowly beginning to look less like a standalone feature…

…and more like another layer of modern software architecture.

Not replacing backend systems.

But integrating deeply into them.

The more I explored modern AI applications, the more familiar the problems started feeling.

Because once you move beyond the demo layer, AI systems suddenly involve things backend engineers already spend years dealing with:

request orchestration
retries and fallbacks
latency optimization
caching
rate limiting
observability
authentication
memory/context handling
distributed workflows
scalability under load

At some point it clicked for me:

A lot of modern AI engineering is still fundamentally systems engineering.

Just with a new layer added on top.

One thing I misunderstood initially was thinking AI engineering was mostly about prompts and models.

But honestly, what’s becoming more interesting to me is everything around the model.

Things like:

RAG pipelines
vector databases
tool calling
AI agents
orchestration layers
context management
enterprise integrations

That’s where backend engineering and AI start blending together.

And I think many backend engineers are actually in a stronger position here than they realize.

If you already understand:

APIs
system design
asynchronous processing
cloud-native systems
distributed architectures
databases and scaling

…you’re already bringing valuable foundations into AI systems engineering.

Right now I’m personally spending time learning:

how LLMs actually work
embeddings and vector search
RAG architecture
agentic workflows
AI system design patterns

Not from a research perspective.

But from a practical engineering perspective.

Because honestly, this shift feels very similar to what happened with cloud adoption years ago.

At first it looked specialized.

Then suddenly it became part of mainstream engineering.

I have a feeling AI may follow a similar path.

Curious how other backend engineers are approaching this right now.

Are you actively learning AI systems yet, or still observing where the industry goes?

I’ll be sharing more practical thoughts around:

backend engineering
AI systems
cloud-native architecture
distributed systems
GenAI engineering

as I continue exploring this space.

Always happy to learn from other engineers building in this area too.