You can’t trust your AI Agent with real users. I make it reliable.
One-time fixes don't work because your agent breaks for new reasons every day. I implement the AI engineering process that teams at OpenAI and Anthropic use to build reliable agents.
It is not better prompts. It is eval-driven development.
The frontier labs do not make agents reliable by writing more instructions. They trace real behavior, turn failures into datasets, and prove every change against them before it ships. That discipline is what I run on your agent.
Trace
Capture every production run, the same observability the labs build first.
Monitor
Score live traffic for failures and drift, so regressions surface in hours.
Build datasets
Turn real failing runs into the eval suite the labs guard like source code.
Experiment
Test every prompt, model, and logic change against that set before users do.
Evaluate
Ship only changes the evals prove are better, the bar frontier teams hold.
Every change is measured, shipped, and fed back in. The agent compounds toward reliable instead of drifting, the way a continuously-evaluated system should.
Satisfied founders who have reliable agents in production.
“I was putting voice agents on real tenant calls and they keep failing in unexpected ways in production. He built the reliability system, fixed what was breaking, and now the agents hold up in production.”
“We needed an agent that answered from multiple enterprise documents with 100% accuracy in arabic. He built the Agentic RAG system, optimized it as per our expectations, and now it is live and working for real customers.”
Questions founders ask me.
Won't it just break again next week?
That is exactly why one-time fixes fail. The eval loop catches new failures before users do, so the agent compounds toward reliable instead of drifting back.
Why not just have my own team do this?
You can. I give you the playbook free. But most teams know they should be running evals and never find the time. I bring the system and the discipline from day one.
My stack is custom. Will this apply?
Yes. The process is stack-agnostic. It works on LangGraph, the OpenAI Agents SDK, the Claude Agent SDK, or your own Node or Python code.
How do I know my data is safe?
I sign an NDA before I touch anything. I work inside your constraints and keep every client fully isolated.
You work directly with me.
I am Moazzam Qureshi. I work on one problem: the gap between an agent that demos well and one a business can actually trust in production. That gap is closed with evals, monitoring, and trajectory discipline, not more prompting.
Seven years across AI and software engineering, working hands-on with founders to take agents from demo to dependable. I implement the reliability process for your agent myself, end to end.