Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Build a comprehensive UX measurement framework for AI agents using CSAT surveys, System Usability Scale, custom behavioral metrics, A/B testing strategies, and analytics pipelines.
Build a comprehensive ROI framework for AI agent deployments. Learn to quantify business value, model costs accurately, track key metrics, and present ROI reports that justify continued investment in agent automation.
Learn how to measure AI support agent effectiveness using CSAT scores, resolution rates, containment rates, and improvement loops that drive measurable gains in customer satisfaction.
Build a memory consolidation pipeline that compresses detailed agent memories into summaries, preserving essential information while reducing storage and improving retrieval quality.
Design AI agents that handle long conversations gracefully by using streaming processing, incremental state management, garbage collection strategies, and memory limits to prevent out-of-memory crashes.
Design secure multi-user memory systems for AI agents with strict user isolation, memory partitioning, encryption at rest, and fine-grained access control to prevent data leakage.
Implement and tune multi-signal memory retrieval for AI agents using recency, relevance, and importance scoring functions with combined ranking and parameter tuning strategies.
Build a version-controlled memory system for AI agents that tracks every change, supports rollback to previous states, and provides audit trails for debugging knowledge issues.
Explore how to use message queues like RabbitMQ, Amazon SQS, and Apache Kafka to manage AI agent workloads with reliable delivery, backpressure handling, dead letter queues, and consumer scaling patterns.
