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Insights on Performance Engineering, AI Agents, and Modern Software Development

Context Engineering: The Discipline That Makes AI Agents Production-Ready

Why production agents fail over context, not intelligence — cache-aware prompt structure, layered information lifecycles, and isolation over compression.

Ashot GrigoryanSeptember 30, 2026

5 min read

AI AgentsEvalsLLM

Agent Evals: Pass@k Is a Demo, Pass^k Is a Product

Capability vs reliability metrics for AI agents — why a 60% step yields 99% Pass@5 but 8% Pass^5, and how to build an evaluation loop that predicts production behavior.

Ashot GrigoryanSeptember 30, 2026

5 min read

AI AgentsArchitectureLLM

When Multi-Agent Architecture Pays Off (and When It Just Burns Tokens)

A practical decision framework for multi-agent AI systems — information isolation over team metaphors, orchestrator topologies, and the metrics that keep costs honest.

Ashot GrigoryanSeptember 30, 2026

Building Production-Ready AI Agents: Lessons from Real-World Deployments — article cover

3 min read

AI AgentsLangGraphLLM

Building Production-Ready AI Agents: Lessons from Real-World Deployments

Practical insights from deploying AI agents in production — architecture patterns, tool integration, error handling, and the LangGraph framework.

Ashot GrigoryanApril 25, 2026

Performance Engineering at Scale: From Detection to Resolution — article cover

3 min read

Performance EngineeringMonitoringBackend

Performance Engineering at Scale: From Detection to Resolution

How we approach performance engineering in production systems — from identifying bottlenecks to implementing scalable solutions that handle millions of requests.

Aleksei SvetlovApril 20, 2026

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