Claude Context Design: What to Include and Leave Out
Design context for Claude agents the right way — what belongs in the window, what to exclude, and why curation beats stuffing for accuracy and cost.
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
Design context for Claude agents the right way — what belongs in the window, what to exclude, and why curation beats stuffing for accuracy and cost.
Connect MCP servers to Claude the right way — authentication, structured error handling, and idempotency that keep enterprise agents safe.
Code-level patterns for structuring Claude agents — sectioned prompts, narrow tools, plan-then-act, and subagents — that survive production.
A concrete, code-level walkthrough to build an enterprise Claude agent from empty repo to a running tool-using loop with MCP and guardrails.
How an enterprise Claude agent really works inside — the model loop, context window, MCP tools, skills, and subagents, wired together end to end.
Easy queries need 3 chunks; hard ones need 30. The 2026 adaptive-K patterns that match retrieval depth to query difficulty.
Postmortems for agentic incidents need new sections. The 2026 retro template for incidents where the LLM was the proximate cause.
Sub-second agent decisions need explicit budgets at every step. The 2026 latency-engineering patterns from real production deployments.
The three-step plan-execute-reflect loop is the spine of every reliable production agent in 2026. The patterns and anti-patterns that decide whether agents survive past pilot.