Kuldeep Paul

Kuldeep Paul

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

Accelerating AI Agent Development with Effective Prompt Management

Accelerating AI Agent Development with Effective Prompt Management

The landscape of AI development has evolved from simple chatbot interactions to sophisticated agent systems that autonomously navigate complex workflows and make critical decisions. As prompt engineering becomes increasingly important, building with language models is becoming less about finding the right words for prompts and more about answering the broader

Ensuring AI Agent Reliability in Production

Ensuring AI Agent Reliability in Production

AI agents are rapidly moving from experimental prototypes to production systems handling critical business processes. Research shows that even the best current AI agent solutions achieve goal completion rates below 55% when working with CRM systems, exposing a fundamental gap between demonstration capabilities and production reliability. Organizations deploying enterprise-wide AI

Exploring the Future of AI Agents: Trends and Innovations in AI Agent Development

Exploring the Future of AI Agents: Trends and Innovations in AI Agent Development

The artificial intelligence landscape is experiencing a fundamental transformation as we progress through 2025. AI agents and AI-ready data have emerged as the two fastest advancing technologies on the 2025 Gartner Hype Cycle for Artificial Intelligence, signaling a shift from generative AI as a standalone capability to autonomous systems that

Incorporating Human-in-the-Loop Feedback for Continuous Improvement of AI Agents

Incorporating Human-in-the-Loop Feedback for Continuous Improvement of AI Agents

The deployment of AI agents to production creates a fundamental challenge: how do you ensure your agents continue improving based on real-world performance rather than static test sets? While automated evaluation provides scalability, human judgment remains essential for capturing nuanced quality dimensions, validating edge cases, and aligning AI behavior with

Auto Evaluation in AI Development: How to Automate the Assessment of Agent Performance

Auto Evaluation in AI Development: How to Automate the Assessment of Agent Performance

Deploying AI agents to production presents a critical challenge: ensuring consistent quality at scale. As AI systems handle thousands of interactions daily, manual quality assessment becomes impractical and introduces bottlenecks that slow down iteration cycles. Auto evaluation (the automated assessment of AI agent performance using predefined metrics and criteria) has

Context Window Management: Strategies for Long-Context AI Agents and Chatbots

Context Window Management: Strategies for Long-Context AI Agents and Chatbots

Context window management has emerged as a critical challenge for AI engineers building production chatbots and agents. As conversations extend across multiple turns and agents process larger documents, the limitations of context windows directly impact application performance, cost, and user experience. Modern language models offer context windows ranging from 8,

Comparing Agent Memory Architectures: Vector DBs, Graph DBs, and Hybrid Approaches

Comparing Agent Memory Architectures: Vector DBs, Graph DBs, and Hybrid Approaches

AI agents require memory systems to maintain context, retrieve relevant information, and make informed decisions. The architecture chosen for agent memory directly impacts performance, retrieval accuracy, and the overall quality of AI applications in production environments. Three primary approaches have emerged for implementing agent memory: vector databases, graph databases, and