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
Fully autonomous agents are still a fantasy in production. LangGraph's interrupt() lets you pause for human approval mid-graph without losing state. We cover approve/edit/reject/respond actions and CallSphere's escalation ladder.
78% of issues resolve via AI bots and 87% of users report positive experiences. Here is how 2026 chat agents fire inline 1–5 stars, NPS chips, and follow-up CSAT without survey fatigue.
Vapi 465ms optimal, Retell 580-620ms, Bland ~800ms, ElevenLabs 400-600ms — but those are best-case. We design a fair benchmark harness, P95 measurement, and a reproducible methodology for 2026.
ITAR technical-data definitions don't care if a human or an LLM produced the output. CMMC Level 2 has been mandatory since November 2025. Here is what an AI voice vendor needs to ship to defense in 2026.
How the modern agent eval stack actually flows: instrument, trace, dataset, evaluator, score, CI gate. The full pipeline that keeps agents from regressing.
Tracing fundamentals for production AI agents — span hierarchy, session correlation, and the failure patterns that only show up when you trace every step.
Beyond single-shot RAG — agentic RAG with LangGraph that re-retrieves, self-grades, and rewrites queries. With evals that catch silent retrieval drift.
Build a browser agent with LangGraph and Playwright that does multi-step web tasks, then ground-truth its work with visual diffs and DOM-based evaluators.
Step-by-step build of a working agent with the OpenAI Agents SDK — Agent class, tools, handoffs, tracing — plus an eval pipeline that catches regressions before merge.
