When to Use Claude — and When Not To (Honest Guide)
An honest guide to where Claude wins, where deterministic code or a human is better, and the hybrid pattern that beats both for enterprise AI.
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
An honest guide to where Claude wins, where deterministic code or a human is better, and the hybrid pattern that beats both for enterprise AI.
Governance, trust, and safety guardrails for scaling Claude — least-privilege tool scoping, eval gates, audit trails, and human-in-the-loop calibration.
Turn a Claude pilot into daily practice — the habits, norms, champions, and CLAUDE.md patterns that drive real organizational adoption of agentic AI.
The real Claude ROI and cost model for enterprises — the three sources of savings, model tiering math, and a 5-step framework to prove value honestly.
A staged playbook to move an existing workflow onto a Claude agent: shadow mode, human-in-the-loop, per-action autonomy, and instant rollback.
Build an eval loop for Claude agents: harvest failures, grade trajectories with programmatic and LLM judges, and gate releases on quality.
Harden Claude agents with sandboxing, least privilege, secrets isolation, and prompt-injection defense for safe production deployment.
Slash Claude agent token cost and latency with prompt caching, model routing, batching, and lean context — without losing quality.
Fix the three failure modes that break Claude agents: runaway loops, wrong tool calls, and hallucinated arguments — with concrete debugging steps.