Blog
Technical articles on LLM routing, agent architecture, code security, compliance, and AI operations. Written by the team behind AI Clarity's developer tools.
Most teams send every LLM call to one provider. That means paying per-token for workloads that could run free, zero budget guardrails, and a single point of failure. Here's how workload-based routing fixes all three.
Stateless retry loops waste 30-40% of LLM spend on tasks that keep failing. Trust gating, outcome history, and supervised dispatch turn blind retries into informed decisions.
A leaked Stripe key costs you rotation time, incident response, and trust. A POSIX pre-commit hook that pattern-matches staged files catches secrets before they enter history.
Enterprise archiving solutions cost $10K+/year. S3 Object Lock in COMPLIANCE mode, a fail-closed spool architecture, and metadata sidecars give you the same guarantees for a one-time $99.
Your LLM output looks fine until a customer gets a proposal with demo data, banned phrases, and pricing in a cold email. Two-layer evaluation catches what regex and LLMs miss alone.
Automated workloads burn through Claude Max quota fast. Two-window tracking, conservation mode that defers high-effort tasks, and per-task cost attribution keep you running all week.