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.
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See how Circini's automated incident management pipeline turns alert emails into triaged Jira tickets using Snowflake Cortex AI, GPT-4.1, Airflow & MS Teams.
Anthropic's Claude Fable 5 and Mythos 5 explained: pricing, availability, frontier benchmarks, the dual-model safeguard architecture, and what they mean for AI agents.
Where Claude Code, MCP, and multi-agent systems are taking GTM engineering next, and how to prepare your team now for standing and multi-agent workflows.
Where Claude Cowork and the Claude agent ecosystem are heading next — standing agents, MCP, skills as a moat — and the concrete moves to prepare your team now.
The metrics, leading signals, and anti-metrics that prove Claude Cowork is working — acceptance rate, time-to-outcome, and why usage counts mislead.
Shipping an agentic GTM workflow is easy; proving it works is hard. The metrics, signals, and eval loops that show a Claude Code rebuild is paying off.
A realistic end-to-end Claude Cowork use case: a quarterly vendor-spend review from vague ask to shipped deliverable, with every agentic step shown.
Follow a complete agentic rebuild of a broken lead-to-meeting pipeline with Claude Code, from messy problem to shipped, monitored workflow.
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