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
Technical comparison of RPA and AI agents covering rule-based vs reasoning architectures, when to use each, migration strategies, and hybrid automation approaches.
How to design tool functions that LLMs can use effectively with clear naming, enum parameters, structured responses, informative error messages, and documentation.
Explore the emerging agent economy where AI agents discover, negotiate with, and transact with other agents using MCP, A2A protocols, and marketplace architectures.
Production resilience patterns for AI agents: circuit breakers for LLM APIs, exponential backoff with jitter, fallback models, and graceful degradation strategies.
Technical guide to GPT-5.4's computer use capabilities for building AI agents that interact with desktop UIs, browser automation, and real-world application workflows.
Build an AI email assistant that reads your inbox, classifies urgency, drafts context-aware responses, and schedules sends using OpenAI Agents SDK and Gmail API.
Architectural comparison of multi-agent topologies including flat, hierarchical, and mesh designs with performance trade-offs, decision frameworks, and migration strategies.
Analysis of Gartner's prediction that 40% of enterprise apps will embed AI agents by late 2026, with a practical implementation guide covering governance, risk management, and architecture.
Analysis of Google Cloud's 2026 AI agent trends report covering Gemini-powered agents, Google ADK, Vertex AI agent builder, and enterprise adoption patterns.
