When to Use a Claude Agent — and When Not To
Honest trade-offs on when an agentic Claude system beats a script, a single prompt, or a human — and when it is the wrong, costly choice.
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
Honest trade-offs on when an agentic Claude system beats a script, a single prompt, or a human — and when it is the wrong, costly choice.
Permissions, auditing, human-in-the-loop, and tool boundaries engineering leaders need before scaling Claude agents safely in production.
The habits, norms, and change management that turn a Claude Code pilot into a daily tool engineering teams actually trust and use.
A grounded cost model for Claude agents — token economics, model routing, prompt caching, and how to measure ROI honestly in production.
Move an existing workflow onto Claude agents without breaking production — shadow mode, incremental cutover, human-in-the-loop, and fast rollback strategies.
Build an eval loop for Claude agents — datasets, deterministic graders, LLM-as-judge, trajectory checks, and CI gates that catch regressions before users do.
Harden Claude agents with sandboxing, least-privilege tools, secret hygiene, and layered prompt-injection defenses — a practical agent security playbook.
Cut Claude agent cost and latency with prompt caching, the batches API, context discipline, and model routing — keep production runs cheap and fast.
Diagnose and fix the top Claude agent failure modes in production — infinite loops, wrong tool calls, and hallucinated arguments — with concrete techniques.