Accelerating Agentic Engineering With Codex at Rootstrap

Client:

Roostrap

Time to First Value:

3 weeks

Users Enabled:

software development team

Our Problem

Problem

Rootstrap’s engineering organization already had a solid foundation in AI-assisted software development. Most engineers were using tools like Cursor and Claude Code day-to-day, and the team understood baseline AI development workflows and engineering patterns. The gap was depth. Few engineers had fully integrated AI into production workflows, customized configurations, built sub-agents, created reusable skills, or connected tools into scalable developer systems. Code review emerged as the largest time sink and the clearest opportunity for leverage. Rootstrap needed a practical way to assess AI fluency across engineers, identify capability gaps, and move teams beyond autocomplete or pair-programming behavior toward more agentic engineering workflows that could improve development speed, review quality, and delivery consistency.

Our Solution

Solution

Eliza delivered a Codex acceleration sprint designed to move Rootstrap engineers from AI-assisted development toward AI-native engineering workflows. The program started with a baseline assessment across developers through 1:1 interviews. Each interview mapped how engineers used AI across coding, code review, workflow automation, configuration, tool orchestration, and production-readiness. The output was a scored view of individual AI fluency plus a synthesis of team strengths, capability gaps, and practical next steps.

Based on the assessment, Eliza ran three focused workshops. The first covered foundations: AGENTS.md setup, reusable skills, commit workflows, and Codex configuration. The second targeted agentic code review—the highest-leverage gap identified in the assessment—showing engineers how to automate review workflows with AI agents and reclaim time from repetitive review tasks. The third covered context management and MCP servers, including context optimization, secure server usage, and strategies to avoid token bloat or oversized context files.

The sprint used Codex as the core enablement layer while acknowledging Rootstrap’s multi-model development environment. The training emphasized transferable agentic engineering patterns including prompt structure, agent task design, context hygiene, review automation, and AI workflow integration.

Our Results

Results

The sprint gave Rootstrap a clear baseline for AI engineering fluency and a practical roadmap for moving engineers toward deeper AI-native development workflows. The immediate impact was diagnostic clarity: Eliza identified where engineers were already strong, where AI adoption remained shallow, and which engineering workflows created the largest leverage opportunity. Code review stood out as the biggest operational bottleneck and became the highest-priority enablement area.

Rootstrap saw ~40% time savings across existing development workflows, with a clear line of sight to 50–60% as adoption expands across code review, setup, context management, and agentic engineering practices. The team also left with a stronger foundation for standardizing AI-assisted software development and AI-native delivery across a broader engineering organization. As one stakeholder put it, the work was “thoughtful, useful, and gives us a good foundation to keep building from.”

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"I really appreciate both the overall feedback and the individual feedback as well from the interviews. It is thoughtful, useful, and gives us a good foundation to keep building from. I also spoke with some of the people who were interviewed, and the feedback I heard was very positive."‍

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-Matias Mansilla