Kamya Shah

Kamya Shah

Prompt Experimentation with Maxim's Prompt Playground

Prompt Experimentation with Maxim's Prompt Playground

TL;DR Prompt experimentation is critical to building reliable, production-grade AI applications. Maxim's Prompt Playground provides teams with a comprehensive platform for iterating, testing, and deploying prompts at scale. Key features include side-by-side prompt comparison, multimodal support, prompt versioning, prompt tools, and seamless deployment workflows. This article explores

Explore how AI prompt experimentation can unlock effective, scalable prompt management

Explore how AI prompt experimentation can unlock effective, scalable prompt management

TL;DR Prompt experimentation turns prompt designing into an engineering discipline: Define hypotheses, execute controlled experiments across models and parameters, assess performance using quantitative and human‑in‑the‑loop metrics, and version and deploy the best‑performing configuration.. With Maxim AI, teams centralize prompt management, automate evals in pre-release and

The Modern AI Observability Stack: Understanding AI Agent Tracing

The Modern AI Observability Stack: Understanding AI Agent Tracing

TL;DR AI agent tracing has become critical for building reliable AI applications at scale. Modern observability stacks enable teams to track agent behavior across multi-step workflows, debug failures in production, and systematically improve quality. This guide explores how distributed tracing adapts to AI systems, covering trace hierarchies, span-level monitoring,

How to run Prompt Experimentations to make better AI Applications?

How to run Prompt Experimentations to make better AI Applications?

TL;DR Prompt experimentation ihelps to improve AI application quality by iterating on prompts, testing across models and parameters, running evaluations, and validating with simulations before deploying. Using Maxim AI’s Playground++, you can version prompts, compare outputs for quality, cost, and latency, run offline/online evals with statistical, programmatic,

Top 6 Reasons Why AI Agents Fail in Production and How to Fix Them

Top 6 Reasons Why AI Agents Fail in Production and How to Fix Them

TL;DR This article explores six primary failure modes: hallucinations, prompt injection vulnerabilities, latency issues from both infrastructure constraints and inefficient agent trajectories, poor tool selection and orchestration, context window limitations causing memory degradation, and distribution shift. While some failures originate from the probabilistic nature of large language models, others

Observability-Driven Development: Using Distributed Tracing to Build Better Multi-Agent Systems

Observability-Driven Development: Using Distributed Tracing to Build Better Multi-Agent Systems

TL;DR Distributed tracing gives end-to-end visibility across multi-agent and microservice workflows, making it practical to debug complex LLM applications, measure quality, and ship with confidence. By adopting observability-driven development with Maxim AI—spanning experimentation, simulation, evaluation, and real-time tracing teams can correlate prompts and tool calls, analyze agent trajectories,

AI Agent Evaluation: Metrics, Strategies, and Best Practices

AI Agent Evaluation: Metrics, Strategies, and Best Practices

TL;DR AI agent evaluation is critical for building reliable, production-ready autonomous systems. As organizations deploy AI agents for customer service, coding assistance, and complex decision-making tasks, systematic evaluation becomes essential to ensure these agents meet performance standards, maintain alignment with business goals, and operate safely at scale. This comprehensive