DEV Community: Usama Saleem

There is no real “memory” unless YOU manage it.

Usama Saleem — Thu, 19 Mar 2026 12:41:13 +0000

State Management & Context Drift in Multi-Step AI Workflows

This is one of the biggest hidden problems in AI automation systems.

Everything works… until it doesn’t.

Here’s what’s actually happening under the hood 👇

LLMs are stateless by design

Every request is independent.

There is no real “memory” unless YOU manage it.

So in multi-step workflows:
```
  Step 1 generates output

  Step 2 depends on it

  Step 3 modifies it
```
If state is not structured → context breaks.
Context drift starts silently

Over multiple steps:

Important details get dropped

Irrelevant tokens get added

Meaning starts to shift

Result:
```
  👉 Output becomes inconsistent

  👉 Agents behave unpredictably
```
Token limits make it worse

You can’t pass full history forever.

So you:
```
  Trim context ❌

  Summarize ❌
```
Both introduce:
```
  Information loss

  Hallucination risk
```
No single source of truth

In most AI frameworks:

Some data in prompts

Some in memory

Some in DB

This fragmentation causes:
```
  Conflicts

  Outdated context

  Logic errors
```
Multi-agent = exponential complexity

When multiple agents interact:

Each has its own context

Each modifies state differently

Now you get:
```
  👉 State desync

  👉 Conflicting outputs
```

What actually works in production:

      Structured state (JSON schemas, not raw text)

      External memory (DB/vector store as source of truth)

      Explicit state passing between steps

      Context compression strategies (not blind trimming)

Workflow checkpoints + validation

AI workflows don’t fail because of models.

They fail because of state mismanagement.

Fix the state → you fix reliability.

AI #Automation #LLM #Engineering #SaaS

Most people don’t fail at startups because of bad ideas.

Usama Saleem — Thu, 19 Mar 2026 12:30:58 +0000

Most people don’t fail at startups because of bad ideas.
They fail because they overbuild.
Here’s my 30-day MVP system (used in real projects):
Week 1 — Validation + Scope
Talk to 10–15 real users (not friends)
Define 1 core problem (NOT 5 features)
Write a simple PRD (Google Doc)
Week 2 — Backend + Core Logic
Node.js + Express / NestJS
MongoDB or PostgreSQL
Auth (Clerk / Firebase)
Stripe (if monetization needed)
Week 3 — Frontend
Next.js or React Native (depending on platform)
Tailwind for speed
ShadCN UI (don’t design from scratch)
Week 4 — Ship Fast
Deploy on Vercel / Railway
Add analytics (PostHog / Mixpanel)
Launch on Twitter + Reddit + Product Hunt
Rules I follow:
No feature takes > 1 day
No pixel-perfect design before validation
No scaling before users
Your MVP is not your product.
It’s a learning machine.
If you’re spending 3+ months building, you’re already late.
hashtag#buildinpublic hashtag#startup hashtag#MVP hashtag#SaaS hashtag#indiehackers

From APIs to AI Employees: The Shift Toward Agentic Software Architectures

Usama Saleem — Mon, 16 Mar 2026 16:50:31 +0000

For the last decade, software was built around APIs.

Developers created endpoints.
Products connected those endpoints.
Businesses built workflows on top of them.

But something fundamental is changing.

We are moving from APIs → AI Employees.

Instead of software exposing functionality through APIs, we now have AI agents that can understand goals and execute tasks across multiple systems.

Think about it:

Before
• API calls
• predefined workflows
• manual integrations

Now
• AI agents reading documentation
• AI deciding which tools to use
• AI executing multi-step tasks autonomously

This is what people are calling Agentic Software Architecture.

In this world:

Software is no longer just tools.
Software becomes workers.

An AI agent can:

• query databases
• call APIs
• browse the web
• analyze documents
• trigger workflows
• communicate results

All without human orchestration.

The interface is no longer buttons and dashboards.
The interface becomes intent.

“Generate the report.”
“Analyze customer churn.”
“Find growth opportunities.”

And the AI does the rest.

The companies that understand this shift early will design AI-native products, not just add chatbots on top of existing SaaS.

The question founders should ask is:

Are you building software tools… or AI employees?

How We Built a Voice AI Agent That Replaces a $75,000 Employee Role

Usama Saleem — Sat, 14 Mar 2026 12:33:18 +0000

The way businesses operate is changing faster than most people realize.

Recently, we built a voice AI agent capable of replacing a role that traditionally costs companies around $750,000 per year when you include salaries, benefits, and operational overhead.

At first, it sounds unrealistic.

But when you look at the numbers, it makes sense.

The Problem
Many companies rely heavily on human teams to manage tasks like:

answering customer calls
qualifying potential leads
scheduling appointments
responding to common question
routing requests to the right department
These processes require large support teams working in shifts.

Even then, businesses still lose opportunities because of:

missed call
delayed responses
inconsistent service quality
The Solution: Voice AI Agents
AI voice agents change this completely.

Become a Medium member
Our system can:

Answer calls instantly
Understand natural language
Ask qualifying questions
Book meetings automatically
Integrate with CRMs and business tools
Operate 24/7 without downtime
Instead of scaling teams, companies can scale AI infrastructure.

Why This Matters
This isn’t just about cost reduction.

It’s about speed and efficiency.

Businesses that adopt AI agents can:

respond to customers instantly
capture every lead
operate around the clock
reduce operational costs dramatically
The gap between AI-enabled companies and traditional businesses will grow quickly.

The Future
Voice agents are just the beginning.

In the coming years we’ll see:

AI sales teams
AI support departments
AI operations managers
The question is no longer if AI will change businesses.

The real question is:

Who will adopt it first?

AI Voice Agents are a game changer for businesses.

Usama Saleem — Fri, 13 Mar 2026 14:54:42 +0000

AI Voice Agents are a game changer for new businesses.

They can:
• Answer calls 24/7
• Book appointments automatically
• Qualify leads instantly
• Reduce hiring costs

One AI agent can do the work of 3–4 employees without breaks.

For startups, this means scaling faster with less cost.

The future of customer support is AI + automation.

Vibe coding is great for prototypes.

Usama Saleem — Thu, 12 Mar 2026 15:40:27 +0000

Vibe coding is great for prototypes.

But it breaks when you hit security and payments.

AI writes code that works — not code that survives attacks.

Common problems:
• No auth checks
• Hardcoded secrets
• Fake payment success flows
Use AI for speed.
Design security yourself.