Security Hardening for Claude Code in Large Repos
Sandboxing, least privilege, secret handling, and prompt-injection defense for running Claude Code safely against real production codebases.
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 running Claude Code safely against real production codebases.
Keep Claude agent runs cheap and fast with prompt caching, the Batches API, model routing, and ruthless context trimming. A founder's cost playbook.
Prompt caching, batching, and context discipline that keep Claude Code runs cheap and fast on large codebases without sacrificing agentic work quality.
Diagnose the three big Claude agent failure modes — loops, wrong tool calls, and hallucinated args — with reproducible traces and boundary validation.
Why Claude Code loops, picks the wrong tool, or hallucinates arguments in large codebases — plus the concrete fixes that get agentic runs back on track.
What to put in Claude Code's context and what to leave out in large codebases: memory tiers, retrieve over paste, attention budgeting, and the dilution trap.
What to put in a Claude agent's context, what to leave out, and why — a founder's guide to context design that makes agents reliable, not flaky.
Production patterns for connecting MCP servers to Claude: auth, schemas, error handling, and idempotency — the wiring that keeps agents reliable.
Connect MCP servers to Claude Code safely in large repos: scoped auth, constraining schemas, recoverable errors, and idempotency that survives retries.