Triggering Cloud Agents From GitHub, Slack, and the Terminal
Each trigger surface encodes different assumptions about access, accountability, and control.
Sloane Deveraux
Staff Writer
Sloane Deveraux
Sloane is a former research engineer who spent three years evaluating AI coding assistants for an enterprise software lab, giving her an unusually empirical lens on agent capability claims. She now translates benchmark methodology into readable analysis for practitioners who need to make real purchasing and architecture decisions.
7 stories
Audit Trails for AI Agent Tool Calls and Diffs
Capturing the full decision chain—not just final commands—is essential for AI agent accountability.
Sloane Deveraux
Codex vs Claude Code for Production Engineering Workflows
Codex and Claude Code split on where agents run, reshaping production engineering tradeoffs.
Sloane Deveraux
Managed Cloud Agents vs Local Claude Code for Production Use
Production AI agents need infrastructure controls that local setups simply cannot provide.
Sloane Deveraux
Scoped Credential Minting and Revocation in Agent Sandboxes
Scoped credentials tied to agent execution lifetime eliminate lingering authority gaps.
Sloane Deveraux
Managing Shared Git Hooks Across Multiple Worktrees
Hook scripts must use `git rev-parse --git-common-dir` to work correctly across all worktrees.
Sloane Deveraux
Remote Development Environments for AI Coding Agent Workloads
Agent workloads need isolation, ephemeral credentials, and hard cost ceilings.
Sloane Deveraux