Browse older CallSphere articles on AI voice agents, contact center automation, and conversational AI.
From the blog
A realistic end-to-end Claude Code walkthrough — vague complaint to reviewed, tested, deployed fix with MCP grounding and evals in an AI-native team.
Contain agent failures in an AI-native startup — Claude failure modes, blast-radius thinking, sandboxing, and guardrails that keep velocity high.
Failure modes, blast radius, and containment patterns for running Claude Code safely in a large codebase — least privilege, diff caps, and verification gates.
The skills and roles founders must hire for to run an AI-native startup on Claude — spec-writing, evals, and agent orchestration in 2026.
The concrete skills, habits, and hiring shifts engineers need to make Claude Code pay off in a large codebase — decomposition, specs, review, and verification.
How founders take agentic AI from one team to many: shared skills, governed MCP infrastructure, orchestration, and the org design that prevents sprawl.
How to scale Claude Code from one team to many on a large codebase — shared skills, federated guardrails, cost control, and rollout without chaos.
Honest founder trade-offs: where Claude agents clearly win, where they quietly cost more than they save, and the simpler alternatives to try first.
An honest guide to when Claude Code is the right tool for a large codebase and when it isn't — the trade-offs, failure modes, and better alternatives.
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