Where prompt caching is heading and how to prepare
Prompt caching is going automatic, persistent, and framework-native. The trends ahead for Claude agents and how to structure context to be ready.
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
Prompt caching is going automatic, persistent, and framework-native. The trends ahead for Claude agents and how to structure context to be ready.
Where verifiable AI for financial services with Claude is heading — proof-carrying agents, regulator-readable trails, and how to prepare your team now.
Metrics that prove an enterprise Claude agent works: task success, escalation quality, cost per outcome, evals as regression tests, and drift detection.
Caching failures are silent. Track cache hit rate, cost per completed task, latency, and eval pass rate to prove your Claude Code agent is working.
The metrics and signals that prove a verifiable AI agent in financial services works on Claude — escalation precision, calibration, and audit completeness.
One enterprise Claude agent from messy problem to shipped outcome: scoping, MCP tools, Skills, evals, staged rollout, and the metrics that closed it.
End-to-end walkthrough of shipping a verifiable card-dispute agent on Claude — from scoping to shadow mode to audited production rollout.
End-to-end walkthrough of a Claude Code flaky-test agent that only shipped once prompt caching fixed the multi-turn loop economics.
A cached Claude Code prefix is reused everywhere, so one bad line is a fleet-wide bug. Failure modes, blast radius, and containment patterns explained.