Kuldeep Paul

Kuldeep Paul

Agentic AI | LLM | Product Management | Product Marketing | Data Science | SaaS

The Ultimate Guide to Debugging Multi-Agent Systems

The Ultimate Guide to Debugging Multi-Agent Systems

Multi-agent LLM systems represent the next evolution in AI architecture, where multiple specialized agents collaborate to complete complex tasks through distributed reasoning and coordination. These systems promise modular workflows, parallel execution, and emergent intelligence that can tackle problems beyond single-agent capabilities. However, production deployments reveal a sobering reality: debugging multi-agent

Building Reliable LLM Applications: From Manual Validation to Automated Testing

Building Reliable LLM Applications: From Manual Validation to Automated Testing

The adoption of large language models in production systems has created a critical gap in software engineering practices. Traditional quality assurance approaches fail when applied to non-deterministic AI systems, yet the need for reliability remains paramount. According to MIT Technology Review research, organizations that establish systematic testing frameworks for AI

Ensuring Reliability in AI Agents: Addressing Hallucinations in LLM-Powered Applications

Ensuring Reliability in AI Agents: Addressing Hallucinations in LLM-Powered Applications

AI engineering teams face a critical challenge when deploying production agents: hallucinations. When your customer support agent fabricates policy details or your data extraction system invents statistics, the consequences extend beyond technical failures to eroded user trust and compliance risks. For teams building AI applications, addressing hallucinations is not optional,

Top 3 Tools for AI Agent Monitoring in 2025

Top 3 Tools for AI Agent Monitoring in 2025

TL;DR Monitoring AI agents in production is not the same as monitoring traditional applications. It requires tracking reasoning steps, retrieval quality, prompt performance, and safety metrics. This guide explains what makes an AI agent monitoring tool effective in 2025, compares the top platforms, and shares best practices for maintaining

Top 20 LLM Related Terms for 2025

Top 20 LLM Related Terms for 2025

AI agents are transforming the landscape of artificial intelligence, moving beyond simple request-response models to autonomous systems capable of complex reasoning, planning, and execution. As 2025 emerges as the breakout year for AI agents, understanding the terminology surrounding these systems has become essential for AI engineers and product managers. This

How to Ensure Reliability in LLM Applications: A Comprehensive Guide

How to Ensure Reliability in LLM Applications: A Comprehensive Guide

Large language model applications are rapidly moving from experimental prototypes to production systems serving millions of users. However, ensuring reliability in LLM applications presents unique challenges that traditional software engineering practices cannot fully address. According to research from Stanford's AI Index Report, 73% of organizations cite reliability concerns

Complete Guide to RAG Evaluation: Metrics, Methods, and Best Practices for 2025

Complete Guide to RAG Evaluation: Metrics, Methods, and Best Practices for 2025

Retrieval-Augmented Generation (RAG) systems have become foundational architecture for enterprise AI applications, enabling large language models to access external knowledge sources and provide grounded, context-aware responses. However, evaluating RAG performance presents unique challenges that differ significantly from traditional language model evaluation. Research from Stanford's AI Lab indicates that