Kuldeep Paul

Kuldeep Paul

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

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 production AI agents 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 evolving

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

The deployment of production AI agents 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)

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

From Black Box to Glass Box: Achieving Transparency with AI Observability

From Black Box to Glass Box: Achieving Transparency with AI Observability

Modern AI systems (LLM-powered chatbots, RAG pipelines, and autonomous agents) often feel opaque. Teams wrestle with questions like “Why did the agent make that decision?” or “Which prompt or retrieval step caused the failure?” Moving from a black box to a glass box demands systematic instrumentation across the AI stack,

The Evolution of AI Quality: From Model Benchmarks to Agent-Level Simulation in 2026

The Evolution of AI Quality: From Model Benchmarks to Agent-Level Simulation in 2026

Building trustworthy AI no longer stops at model scorecards. In 2026, the standard for AI quality shifts decisively from static model benchmarks to agent-level evaluation, simulation, and observability across real user journeys. Teams need to understand multi-turn decisions, tool calls, retrieval context, and failure recovery, not just whether a model

The Technical Guide to Managing LLM Costs: Strategies for Optimization and ROI

The Technical Guide to Managing LLM Costs: Strategies for Optimization and ROI

LLM costs have become a critical concern for engineering teams deploying AI applications at scale. In 2025, API pricing ranges from $0.25 to $15 per million input tokens and $1.25 to $75 per million output tokens, creating significant budget variability depending on model selection and usage patterns. Organizations