Kamya Shah

Kamya Shah

The Role of Real-Time Analytics in AI Agent Decision Making

The Role of Real-Time Analytics in AI Agent Decision Making

TL;DR Real-time analytics has become the backbone of AI agent decision making in production environments. By processing and analyzing data as it flows through agentic systems, organizations can detect failures, optimize performance, and maintain reliability at scale. This article explores how real-time analytics through distributed tracing, continuous monitoring, and

10 Best Practices for Observability in Distributed AI Systems

10 Best Practices for Observability in Distributed AI Systems

TL;DR Observability in distributed AI systems requires end-to-end tracing across agents, models, and data pipelines; unified logging with structured semantics; reproducible evaluation harnesses; targeted simulations for failure discovery; and continuous, policy-driven quality checks in production. Combine distributed tracing, evaluation workflows, and multimodal data curation with an AI gateway for

Leveraging Contextual Techniques for Improved AI Agent Responses

Leveraging Contextual Techniques for Improved AI Agent Responses

TL;DR Contextual techniques are essential for building AI agents that deliver accurate, relevant responses. This guide explores retrieval-augmented generation (RAG), prompt engineering with context windows, and memory management strategies that enable agents to understand user intent and maintain conversation coherence. Learn how to implement context sources, optimize token usage,

Challenges in Managing High-Quality Datasets for LLM Evaluation

Challenges in Managing High-Quality Datasets for LLM Evaluation

TL;DR Managing high-quality datasets for LLM evaluation presents significant challenges that directly impact model performance and reliability. Research shows that models trained with poor data quality can experience a precision drop from 89% to 72%, demonstrating the critical importance of data curation. Organizations face hurdles including dataset scalability issues,

5 Strategies for A/B Testing for AI Agent Deployment

5 Strategies for A/B Testing for AI Agent Deployment

TL;DR A/B testing for AI agents compares controlled variants of prompts, workflows, and models against defined success metrics. Combine offline simulation, targeted evals, and in‑production observability to detect regressions, quantify impact, and iterate safely. Use an AI gateway for consistent routing, cost and latency telemetry, and repeatable

Top 3 Observability Platforms for AI Agents in 2026

Top 3 Observability Platforms for AI Agents in 2026

TL;DR This guide compares Maxim AI, Arize AI, and LangSmith for AI agent observability in 2026, focusing on features and best-fit scenarios. Maxim AI offers end-to-end simulation, evals, and production-grade observability with multimodal support and cross-functional UX. Arize excels at model observability and ML monitoring for traditional MLOps. LangSmith

Speeding Up the Development Cycle: How to Efficiently Iterate and Deploy AI Agents

Speeding Up the Development Cycle: How to Efficiently Iterate and Deploy AI Agents

TL;DR Organizations deploying AI agents report significant gains: early adopters achieve 2x iteration speed when fine-tuning industry-specific agents, while 62% of organizations expect more than 100% ROI from agentic AI deployment. This article explores proven strategies to accelerate AI agent development through systematic experimentation, automated simulation, comprehensive evaluation frameworks,