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 production-grade Grafana dashboards for AI agent systems that visualize conversation throughput, per-model costs, LLM latency percentiles, and tool usage patterns using Prometheus metrics.
Learn when and how to build a custom agent orchestrator. Covers state machine design, queue integration, monitoring hooks, and the architectural patterns that make custom orchestrators maintainable.
Learn how to design analytics report templates for AI agents, aggregate performance data into meaningful summaries, generate HTML and PDF reports, and deliver them on schedule via email and Slack.
Build a complete customer support multi-agent system with four specialized agents — a router, FAQ handler, billing specialist, and escalation agent — using the OpenAI Agents SDK with shared context and graceful fallbacks.
Learn how to build an AI agent that converts natural language questions into SQL queries, executes them against a database, generates charts from the results, and provides plain-English interpretations of the data.
Design and implement multi-step data extraction pipelines that transform unstructured text into clean structured data using LLMs. Covers entity extraction, relation extraction, and pipeline orchestration.
Build an intelligent ETL pipeline agent that uses LLMs to infer schemas from messy data, transform records with natural language instructions, and validate data quality at each stage.
Learn to build an AI agent that generates safe database migrations from natural language requirements. Covers schema analysis, migration generation, safety checks, rollback planning, and testing strategies.
Learn how to build a comprehensive debug mode for AI agents with toggle-able verbose logging, step-through execution callbacks, state dumps, and conversation replay capability for efficient troubleshooting.
