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AI Agents don’t usually fail because the LLM is weak.
Most of the time, they fail because the system around the LLM isn’t designed for production.
A demo agent can look impressive with just a prompt and a few tools.
But production changes everything.
Your agent needs:
→ Reliable context management
→ Persistent memory
→ Proper tool selection
→ Retry and fallback logic
→ Error handling
→ Observability and logging
→ Clear boundaries for decision-making
Without these layers, even a powerful model can become unreliable.
This is one of the biggest differences between building an AI demo and engineering an AI product.
The LLM is important, but it’s only one part of the architecture.
Production AI is not just prompt engineering. It’s system engineering.
What has been the biggest challenge for you while building AI agents?
Want to see how this works in a real AI agent?
I’ve explained the complete concept of context engineering, the biggest reasons AI agents lose context in production, and the practical approach I use to solve these problems in my latest YouTube video.
▶️ Watch the full video on YouTube - https://www.youtube.com/watch?v=XAnu2WJ9tRk
#AI #AIAgents #AgenticAI #SoftwareEngineering #GenerativeAI
Absolutely agree. Production AI is much more about system engineering than just prompt engineering. Reliability, memory, observability, and fallback strategies make all the difference. 🚀
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I build production-ready AI systems that combine LLMs, RAG, reinforcement learning, advanced agentic architectures, automation to transform complex workflows into intelligent, scalable products. I have also built some MVP using agentic AI and open to share it with investors.
What I build:
→ AI Agents & complex multi-agent systems
→ Agentic AI architectures & autonomous workflows
→ RL-based LLM reasoning & reasoning optimization
→ RAG / knowledge assistants & enterprise search
→ LLM-powered applications
→ Document & data intelligence
→ AI automation & workflow orchestration
→ NLP & Generative AI solutions
→ AI-driven decision-making systems
→ Frontend and Backend using Nextjs, Reactjs and FastAPI
Core expertise:
LLMs • RL for LLM Reasoning • Agentic AI • Multi-Agent Systems • RAG • Generative AI • NLP • Reinforcement Learning • Knowledge Graphs • Vector Search • AI Automation • AI Architecture
Tech stack:
Python • PyTorch • LangGraph • LlamaIndex • OpenAI • AWS • Vector Databases • Docker • Kubernetes • React • TypeScript • fastAPI • Nextjs
I focus on the full AI lifecycle from problem formulation, AI architecture, and rapid prototyping to model development, agentic workflow design, deployment, evaluation, and production optimization.
My goal is to design AI systems that don't just generate content, but reason, retrieve knowledge and use tools for computations, coordinate agents, learn from feedback, and execute complex tasks autonomously with automatic validation and assessment.
I need more GPU and investments to develop more vertical AI products.