Risk management for Claude agents in finance
Failure modes, blast radius, and containment patterns for verifiable AI agents in financial services built on Claude. Bound what an agent can break.
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
Failure modes, blast radius, and containment patterns for verifiable AI agents in financial services built on Claude. Bound what an agent can break.
Failure modes, blast radius, and guardrails for enterprise Claude agents — scoped tools, approvals, injection defense, and live monitoring.
Prompt caching reshaped Claude Code teams. The new context-engineer role, skills to relearn, and how to interview for cache-aware agent design.
The roles and skills financial-services teams need to build verifiable AI agents on Claude — reliability engineers, eval discipline, and compliance fluency.
The skill shifts and hiring moves for enterprise Claude agents: context engineering, eval design, MCP integration, and agent operations.
How to grow verifiable AI for financial services with Claude from one team to many — shared platform, reusable controls, no chaos.
Shared skills, MCP catalogs, central evals, and platform patterns to scale Claude agents from one team to many without chaos.
Scale Claude Code across an organization in 2026 without chaos: shared skill libraries, default guardrails, model routing, and the caching dividend.
An honest map of where Claude Code wins and where it doesn't in 2026 — the tasks to keep human, simpler alternatives, and the over-reliance trap.