Building AI Pipelines with Directed Acyclic Graphs (DAGs)
A deep technical guide to designing AI and LLM processing pipelines using DAG-based architectures for reliable, observable, and scalable agentic workflows.
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
A deep technical guide to designing AI and LLM processing pipelines using DAG-based architectures for reliable, observable, and scalable agentic workflows.
How to roll out Claude Code across a development team — shared CLAUDE.md, custom commands, permission policies, cost management, onboarding, and team-wide standards.
Compare CallSphere and Smith.ai for AI voice agents. See features, pricing, compliance, and which platform is better for your business.
Skills are reshaping how Claude agents do specialized work. Where the capability is heading in 2026 and how engineering teams can prepare now.
Prove a Claude agent built with Skills is working: outcome metrics, quality signals, eval gates, and cost-per-outcome that separate value from demos.
A realistic end-to-end walkthrough of building a Claude agent with Skills — from a messy support backlog to a deployed, trusted, production agent.
Skills give Claude agents real actions and real failure modes. A practical guide to blast radius, containment controls, and safe staged rollout.
Building agents with Claude Skills reshapes roles and hiring. The new responsibilities, skills engineers must learn, and how to close the gap fast.
Scale Claude Agent Skills from one team to the whole org — shared libraries, ownership, versioning, and a federated model that avoids chaos.