The author of a 20-year-old insurance platform ran 108 support tickets through a custom AI-agent pipeline and the result is a workflow that turns a multi-hour, senior-developer task into a handful of minutes, a shift that could reshape how enterprises keep legacy code alive.

Why legacy systems matter more than new code

The insurance application in question is a monolith of 2.3 million lines of code and roughly 1,000 PL/SQL packages. Its size alone makes it impossible for any single person to own the whole codebase. Add to that a maze of customer-specific configuration parameters, scattered documentation, and a ticket archive that stretches back to 2017, and the real bottleneck becomes “finding context,” not writing code.

Typical modern AI hype focuses on generating fresh code for greenfield projects. In this case the hard part is not the syntax of PL/SQL but locating the exact piece of logic, the relevant configuration, and the historical ticket that first described the problem. An experienced developer can spend hours stitching together clues from GitLab, SVN, wikis, and old support tickets. The AI agent does the same work in minutes.

The workflow in practice

When a new ticket lands, the author runs a single command. The agent then:

  • Pulls the ticket text and any attached files through the ticket-system API.
  • Executes a keyword and vector search across the entire ticket archive to surface similar past cases.
  • Queries a personal library of reusable SQL scripts.
  • Inspects code history in the version-control systems (GitLab or SVN).

All findings are compiled into one file that also suggests the next step—usually a code fix, a draft reply to the customer, or a request for additional diagnostics.

Built-in capabilities

The author has defined 24 “skills” for the agent, grouped into four categories:

  • Context access – reading APIs, manuals, and databases to pull relevant facts.
  • Domain knowledge – interpreting insurance accounting rules and the system’s architecture.
  • Writing – generating PL/SQL snippets and packaging them for deployment.
  • Meta – recognizing patterns and automatically creating new skills when needed.

These skills let the agent act as a junior engineer that never sleeps, surfacing the exact line of code or configuration that a ticket references.

Safety nets built into the loop

Automation in a production environment demands safeguards. The author follows two simple rules:

  1. Static validation – every generated script is run through an EXPLAIN PLAN against the live schema. This checks for syntax or logical errors without actually executing the code.
  2. Dual-model confirmation – a second, independent AI agent reviews any change deemed risky. If both models arrive at the same conclusion, the author proceeds; otherwise, the ticket is escalated for manual review.

These checks keep the process from becoming a black box that could inadvertently break a critical insurance transaction.

Compounding benefits

Each ticket’s output is attached back to the ticket record, creating a living knowledge base. When a similar issue resurfaces months or years later, the agent can read not only the previous solution but also the reasoning that led to it. In effect, every resolved ticket becomes training data for future tickets, accelerating the cycle further.

Honest limitations

  • Manual testing remains – the author still validates changes in a test environment before promotion.
  • No hard-stop metrics – while the time saved feels substantial, the author has not quantified the exact reduction in hours.
  • Personal setup – the current implementation lives on a single workstation; scaling it across a team would require additional engineering.

These constraints keep the approach from being a turnkey product, but they do not diminish the core insight: AI can collapse context gathering from hours to minutes.

What to watch next

The author’s experiment is a proof-of-concept rather than a commercial offering. The next logical steps include:

  • 指标标准化 – 追踪引入 AI 流水线前后工单的解决时间，以构建业务案例。
  • 团队部署 – 将智能体（agent）封装为共享服务，使多名工程师能够从同一个知识库中受益。
  • 与 CI/CD 集成 – 将验证过的脚本直接输入到持续集成流水线中，可以实现从工单到生产环境的闭环，无需人工交接。

如果这些扩展得以成功实施，该模型可以成为其他正在应对庞大且根深蒂固代码库的企业所借鉴的模板。

核心总结

AI 在遗留环境中的真正价值不在于自动编写新代码，而在于即时呈现正确的上下文。通过将资深开发人员数小时的“侦探式”排查工作缩短至几分钟，AI 智能体工作流可以保持陈旧系统的功能性，降低支持成本，并逐步构建一个自我强化的知识库。实验表明，对于遗留软件而言，最大的生产力提升来自于缩短寻找答案的过程，而非生成全新的代码。