Evals for Claude Agents: Measure Quality, Gate Releases (Cowork Enterprise Ready)
Build an eval loop for Claude agents — real datasets, deterministic graders, LLM-as-judge, and CI regression gates that catch quality drops before release.
Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
From the blog
Build an eval loop for Claude agents — real datasets, deterministic graders, LLM-as-judge, and CI regression gates that catch quality drops before release.
Harden Claude agents: sandbox tools, enforce least privilege, keep secrets out of context, and defend against prompt injection from untrusted content.
Make Claude agents cheap and fast with prompt caching, the Message Batches API, model routing across Opus/Sonnet/Haiku, and tight context discipline.
Diagnose and fix the three big Claude agent bugs — infinite loops, wrong tool calls, and hallucinated arguments — with traces, schemas, and guardrails.
Prompt and context design for enterprise Claude Cowork: what to put in context, what to retrieve on demand, and how to cache the stable prefix.
Wire tools and MCP servers into enterprise Claude Cowork: least-privilege auth, strict schemas, structured errors, and idempotent mutations.
Reusable enterprise Claude Cowork patterns: single-purpose skills, structured tool contracts, stratified context, and idempotent actions.
Step-by-step: roll out Claude Cowork to an enterprise team with a scoped plugin, least-privilege connectors, checkpoints, and real validation.
Inside Claude Cowork's enterprise architecture: how the planning model, plugins, MCP connectors, and sub-agents fit together end to end.
Get notified when we publish new articles on AI voice agents, automation, and industry insights. No spam, unsubscribe anytime.
Try our live demo -- no signup required. Talk to an AI voice agent right now.