Insights from the field
Practical writing on production AI systems, operating models, and the engineering work required to make agents dependable.
Agentic AI Architecture Patterns for Production Teams
A practical guide to common agent architecture patterns, when they fit, and the trade-offs teams should evaluate before deploying them.
Planning the Real Cost of AI Agents in Production
A budgeting guide for AI agents that covers model usage, infrastructure, evaluation, support, and the hidden operating costs teams often miss.
AI Agent Security in Production
Security risks for AI agents, including prompt injection, data exposure, tool abuse, and the controls teams should implement before launch.
How to Evaluate AI Agent Performance
A practical evaluation framework for correctness, reliability, latency, cost, and safety in production AI agent systems.
Common AI Agent Failure Modes and How to Debug Them
A practical taxonomy of production agent failure modes, with guidance for tracing the root cause and choosing the next fix.
A Practical Playbook for Reducing LLM Spend
Prompt compression, routing, caching, and better observability can reduce waste without turning quality into guesswork.
EU AI Act Readiness for Enterprise AI Agents
Technical controls teams should consider as they prepare AI agent programs for transparency, oversight, logging, and governance requirements.
Why RAG Pipelines Fail in Production
A debugging guide for chunking, embeddings, freshness, context assembly, and evaluation gaps in enterprise RAG systems.
Moving an AI Agent from Prototype to Production
What teams usually need to add around a working prototype before it can be trusted in production.
LangChain vs CrewAI vs AutoGen
A framework comparison focused on orchestration style, flexibility, debugging, and fit for enterprise delivery teams.
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