CI Pipeline Configuration for AI Agent Testing Workflows
Stabilize environments first, then measure what your AI agent actually does.
Elise Cho
Build Stack Review — Page 5
CI/CD Pipeline Design for AI Agent Task Execution
Treat agent runtimes with the reproducibility discipline applied to production code.
Ravi Petrova
Dependency Provenance in AI-Generated Code
AI-generated code suggests packages that often don't exist, opening a supply chain attack vector.
Dana Kovac
Tool Availability Contracts for MCP-Native Agent Workflows
Agent failures usually stem from missing tools, not broken reasoning.
Elise Cho
Sandboxing AI Agents That Execute Code Locally
Containers alone won't protect against AI-generated code escaping to the host.
Ravi Petrova
Sandboxing AI Agents to Prevent Filesystem and Network Abuse
Declarative controls layered over containers stop agents from abusing filesystems and networks.
Ravi Petrova
MCP Server Environment Dependencies and Packaging
How six distribution paths create reproducibility gaps in MCP server deployments.
Dana Kovac
Reproducible Environments for AI Coding Agents
Only 68% of agent-generated code runs in clean environments without modification.
Elise Cho
Environment Reproducibility Auditing in Existing Codebases
Only 25% of declared environments actually reproduce, exposing supply-chain and reliability risks.
Dana Kovac
Hermetic Builds and Why Non-Hermetic Builds Break CI Reliability
Hermetic builds eliminate hidden dependencies that silently break CI pipelines.
Ravi Petrova
Reproducible Environments on Apple Silicon and x86 Teams
Teams now need both Apple Silicon and x86 architecture, making reproducible environments essential.
Dana Kovac
Declarative Environment Definitions and Hermetic Builds
Declaring every dependency and tool upfront is the only way to build reliably everywhere.
Elise Cho