Security Hardening for Claude Agents in Banking Systems
Sandboxing, least privilege, secret handling, and prompt-injection defense for Claude financial-services agents that touch money and sensitive data.
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
Sandboxing, least privilege, secret handling, and prompt-injection defense for Claude financial-services agents that touch money and sensitive data.
Harden enterprise Claude agents with sandboxing, least-privilege tools, secrets isolation, and layered prompt-injection defense that keeps a fooled agent harmless.
Keep Claude agent runs cheap and fast with prompt caching, batching, context pruning, and model routing — the token-cost levers that matter in production.
Caching, batching, and context strategies that keep Claude financial-services agents cheap and fast without sacrificing the verifiability the domain demands.
Keep enterprise Claude agents cheap and fast with prompt caching, transcript pruning, batching, and model routing — without sacrificing answer quality.
How to debug loops, wrong tool calls, and hallucinated arguments in Claude financial-services agents — with a reproducible failure-mode triage workflow.
Diagnose and fix the top enterprise Claude agent failures — infinite loops, wrong tool calls, and hallucinated arguments — with traces, guardrails, and regression tests.
Why Claude agents loop, pick the wrong tool, or hallucinate arguments — and the concrete instrumentation and prompt fixes that make agentic runs reliable.
What to put in a Claude agent's context, what to leave out, and why — designing prompts for cacheability, signal density, and a warm, stable prefix.