Hiring and Skills for Claude Agents in Financial Services
The concrete skills, new roles, and retraining tracks financial-services teams need to deploy Claude agents that actually ship and pass audit.
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
The concrete skills, new roles, and retraining tracks financial-services teams need to deploy Claude agents that actually ship and pass audit.
Quantization: How to Choose the Right Precision for LLM Inference
Grow Claude from one team to a whole financial-services organization without chaos — the platform, ownership, and reuse patterns that keep scaling sane.
An honest look at Claude's trade-offs in financial services — where agentic AI wins, where deterministic code or traditional models win, and how to decide.
The trust, safety, and governance controls leadership must build before scaling Claude in regulated finance — audit trails, action boundaries, and eval gates.
Turn a Claude pilot into a lasting team habit in banking and insurance — the change-management norms, rituals, and metrics that make agentic AI stick.
A grounded cost model for Claude in banking and insurance — where time and money savings actually come from, token economics, and defensible payback periods.
A staged playbook for moving financial workflows onto Claude agents — shadow mode, human-in-the-loop, and incremental rollout that limits blast radius.
Build an eval loop that measures Claude financial-agent quality and gates releases — scoring tool calls, outcomes, and regressions before production.