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
Current state of AI agent safety research covering alignment techniques, sandbox environments, constitutional AI applied to agents, and red-teaming methodologies.
How AI sales agents automate BDR workflows with inbound lead qualification, outbound batch calling campaigns, real-time transcription, lead scoring, and CRM integration patterns.
Build a three-agent data pipeline with ingestion, transformation, and analysis agents that process data from APIs, CSVs, and databases using Python.
Practical guide to building agentic AI systems with Claude Code CLI — hooks, MCP servers, parallel agents, background tasks, and production deployment patterns.
Managing context in long-running AI agents: conversation summarization, selective pruning, sliding window approaches, and when to leverage 1M token context versus optimization strategies.
How AI agents interact with databases safely using read-only tools for queries, write-through validation layers, and event-driven updates via message queues.
Technical guide to Kubernetes deployment for AI agents including container design, HPA scaling, readiness and liveness probes, GPU resource requests, and cost optimization.
Why enterprises are shifting from generalist chatbots to domain-specific AI agents with deep functional expertise, with examples from healthcare, finance, legal, and manufacturing.
How to run AI agents on edge devices using NVIDIA Nemotron, Meta Llama, GGUF quantization, local inference servers, and offline-capable agent architectures.
