Prompt and Context Design for Enterprise Claude Agents
What to put in a Claude agent's context and what to leave out: a practical guide to context engineering for reliable, cost-efficient enterprise agents.
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
What to put in a Claude agent's context and what to leave out: a practical guide to context engineering for reliable, cost-efficient enterprise agents.
Design Claude finance-agent prompts and context for verifiable, compliant answers: what to include, what to exclude, and how to prune across a conversation.
Connect tools and MCP servers to Claude agents the right way: scoped auth, tight schemas, structured error handling, and idempotent writes for safe tool use.
Wire MCP servers into a Claude finance agent the right way: scoped auth, strict schemas, typed error handling, and idempotency for money-moving tools.
Connect MCP servers to Claude with auth, schemas, error handling, and idempotency that keep the prompt cache warm instead of silently invalidating it.
Reusable code-level patterns for Claude agents: layered prompts, typed tool schemas, structured output validation, context budgeting, and human escalation.
Reusable prompt, tool, and context patterns for verifiable finance agents on Claude that keep every claim grounded, tagged, and auditable.
Reusable patterns for cache-friendly Claude agents: append-only logs, deterministic assembly, volatility ordering, and layered cache breakpoints.
An engineer-followable walkthrough for building a production Claude agent: the loop, tool schemas, gated writes, retries, idempotency, evals, and rollout.