A practitioner's guide

Running coding agents in enterprise codebases

Most enterprise agent failures are not model failures. They are systems failures.

This guide covers the operational patterns teams are using to run coding agents reliably across large codebases, including context engineering, MCP-based retrieval, review pipelines, artifact-driven workflows, and human oversight practices.

Key takeaways

  1. 1

    Understand why coding agents fail in enterprise environments

    Learn the operational challenges that emerge when agents work across large, complex codebases and why reliability depends on more than the model itself.

  2. 2

    Improve how agents retrieve and use codebase context

    See how teams are approaching context engineering, MCP-based retrieval, and code intelligence to help agents navigate enterprise repositories more accurately.

  3. 3

    Build safer, more scalable agent workflows

    Explore practical patterns for review pipelines, durable work records, and human oversight that help teams run coding agents more reliably in production.

  4. 4

    Reduce review burden and protect developer attention

    Learn strategies for managing agent-generated output without overwhelming engineering teams with noisy reviews and operational overhead.

  5. 5

    Create a foundation for long-term agent adoption

    Get a practical framework for moving from experimentation to sustainable enterprise deployment.