Guardrails in AI: Keeping LLMs Safe

Sowjanya Sankara — Mon, 27 Apr 2026 17:11:09 +0000

🤔 Imagine asking an AI agent to generate a database query…
and it returns something wrong — or worse, unsafe.

The problem isn’t just intelligence.
It’s control.

That’s where guardrails come in.

⚡ What are Guardrails in AI?

Guardrails are checks and controls added around an AI system to ensure it behaves correctly, safely, and reliably.

They don’t make the model smarter.
They make the system trustworthy.

Guardrails don’t change what the model knows — they control how it behaves.

Think of guardrails as:

Filters before the model runs
Validators after the model responds
Rules that guide system behavior

⚙️ Where Do Guardrails Fit?

AI systems are not just:
User → Model → Response ❌

They actually work like this:
User → Input Guardrails → Model → Output Guardrails → Final Response ✅

Before the model → validate input
After the model → validate output

Guardrails sit outside the model, not inside it.

🧩 Types of Guardrails

🔹 Input Guardrails
Ensure the user input is safe and valid.

Block harmful or malicious prompts
Prevent prompt injection attempts
Validate structure of input

👉 Example:
User tries to override system instructions → blocked

🔹 Output Guardrails
Ensure the model output is usable and correct.

Validate format (JSON, query, etc.)
Filter unsafe or irrelevant content
Check for missing or incorrect fields

👉 Example:
LLM generates an invalid MongoDB query → rejected or retried

🔄 Guardrails in AI Agents

In agent systems, guardrails are applied at multiple steps:

Before understanding the query
Before calling a tool
After generating a response

Guardrails are not a single step — they are layered across the system.

⚠️ Why Guardrails Matter

Without guardrails:

Models can hallucinate
Outputs can be incorrect
Systems can behave unpredictably

With guardrails:

Responses become reliable
Systems become safer
Results become consistent

💡An AI system without guardrails is not ready for real-world use.

🔍 Real-World Example

User asks:
“Find transfer passengers under 24 hours”

🔹 Before the model (Input Guardrails)
Check if the request is valid
Ensure required conditions are present (like time constraint)
Prevent unsafe or irrelevant instructions

👉 Input is cleaned and structured before reaching the model

🔹 After the model (Output Guardrails)
Validate the generated query format
Ensure required filters (like “under 24 hours”) are applied
Check logic before execution

👉 Output is verified before being used

Conclusion

Building AI isn’t just about generating outputs.
It’s about making sure those outputs are correct, safe, and usable.

That’s what guardrails enable.

How AI Agents Use Short-Term and Long-Term Memory

Sowjanya Sankara — Tue, 14 Apr 2026 07:22:18 +0000

Have you ever wondered why you forget a phone number in seconds but remember your childhood memories forever? 🤔

That’s not random — it’s how our brain is designed.
We rely on two powerful memory systems:

Short-Term Memory (STM) handles what’s happening right now
Long-Term Memory (LTM) stores what matters over time

Interestingly, modern AI agents work in a very similar way.

In this blog, we’ll explore how AI agents use STM and LTM—and how they orchestrate both to make intelligent decisions.

What is Short-Term Memory (STM) in AI Agents?

Short-Term Memory in AI agents refers to temporary memory that is used during an ongoing conversation or a task

Think of STM as:
🧠 What the agent is currently thinking about?

Current user question
Conversation history
Temporary variables during execution

In Our usual terms:
When a chatbot responds to you it remembers:

What you just asked
What it replied back

But this memory is not permanent - once the session ends Boom! It's memory is gone. ( Like Gajini 🫠)

What is Long-Term Memory (LTM) in AI Agents?

Long-Term Memory stores information that persists beyond a single interaction.

Think of LTM as:
🫀 What the agent has learned over time?

Stored Documents (vector databases)
Knowledge bases
Past interactions when saved
RAG systems

In Our usual terms:
When a chatbot answers based on

Company documents
Previously stored knowledge

…it is using Long-Term Memory.

⚙️ How Agents Orchestrate STM and LTM

This is where things get interesting...

AI agents don’t just use memory—they coordinate (orchestrate) between STM and LTM.

Let us take a real world Example

Let’s say:

👉 User asks:
“Find infant passengers at DEL within 24 hours.”

What happens:

STM:
- Understands the current request
- Keeps the conversation context
LTM:
- Provides stored logic and rules
- Knows how to identify infant passengers based on stored memory
Orchestrator:
- Picks the right data
- Applies the logic
- Builds and runs the query

💡 In one line:
STM = current thinking, LTM = stored knowledge, orchestration = connecting both

🚀 Final Thoughts

AI agents are becoming more powerful not just because of better models—but because of better memory systems.

Understanding how STM and LTM work together helps us:

Build smarter systems
Design better orchestrators
Improve user experience

Forem: Sowjanya Sankara

Guardrails in AI: Keeping LLMs Safe

How AI Agents Use Short-Term and Long-Term Memory