Build a Verifiable Claude Finance Agent: Walkthrough
A step-by-step guide to building a verifiable financial-services agent on Claude: deterministic tools, evidence capture, a verifier loop, and policy gating.
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
A step-by-step guide to building a verifiable financial-services agent on Claude: deterministic tools, evidence capture, a verifier loop, and policy gating.
A step-by-step walkthrough to add prompt caching to a Claude agent loop: structure requests, place breakpoints, prove hits, and avoid cold starts.
How enterprise AI agents on Claude fit together end to end: the model loop, MCP tools, memory, guardrails, and where the model deliberately doesn't go.
How prompt caching works inside Claude Code: cache breakpoints, prefix matching, TTLs, and the append-only loop that keeps agents fast and cheap.
How the layers of verifiable AI for financial services fit together on Claude: orchestration, deterministic tools, an evidence ledger, and policy gates.
A practical engineering deep dive into Claude Code 2.1 Tel Aviv, covering architecture, tradeoffs, and what production teams need to know about cybersecurity AI.
A practical engineering deep dive into Claude memory vs RAG, covering architecture, tradeoffs, and what production teams need to know about agent architecture.
Arize Phoenix is the open-source LLM observability tool that grew up significantly in 2026. Tracing, evals, and the OTel-native approach that makes Phoenix portable.
Inngest's Agent Kit adds durable steps, retries, and concurrency control for agent runs. The right pick for agents that span hours or days without losing state.