Internship - Build an Agentic Software Refactoring Workflow

Standard (Mon-Fri)

Office

Software development requires substantial effort across requirements, design, coding, testing and review. Repeated handovers create overhead, inconsistencies and rework. AI can reduce this effort, but code generation alone is insufficient. Software must meet user needs, engineering standards and quality expectations under normal and adverse conditions. We want to test whether specialized AI agents can handle the full V-model through coordinated work, recurring feedback and human oversight.

Build a proof of concept that demonstrates the complete multi-agentic workflow on a concrete software improvement and measure productivity gains against quality, human effort and operating costs. A faster output using AI only matters if it meets the agreed standards. As such, the expected benefits are:

• Reduce routine specification, implementation, testing and review effort.
• Give engineers more time for requirements, architecture, design and quality decisions.
• Build practical AI experience and stay ahead in software engineering.
• Establish evidence for wider adoption.

An intern with strong software engineering and great motivation and enthusiasm to work with Artificial Intelligence (AI) in SW development will design, implement and evaluate specialized agents for requirements analysis, functional specification, high-level design, detailed design, coding, unit testing, integration testing, system testing and user acceptance testing. Agents must improve their outputs iteratively, exchange feedback across stages and meet defined completion criteria before human-approved handover.

• Scope: Refactor Acquisition Service Tool and/or Reference Image Manager to improve maintainability, reuse and extensibility. Enable feature migration and reuse across AST, RIM, Sherpa and MiCo, supporting the retirement of Acquisition Monitoring as Service Tool.
• Duration: six months, supported by a software engineering mentor and a testing or quality representative.
• Deliverables:
  • A working, repeatable workflow and demonstrated software improvement:
    • The architecture of AST has been significantly improved so that it is made more modular, easy to maintain, reusable across different projects and extendable, so that adding extra new functionality can be done faster and more efficiently by the SW teams.
    • The current functionality and behavior available in AST is maintained, that is, no difference with current solution.
    • Extensions to the existing testing suit as needed to secure zero regression impact.
  • Agent instructions, skills, context packages, memory and handover rules.
  • Linked requirements, designs, code, test plans and execution evidence, meeting the quality expectations and the internal definition of done
    • Any gaps in requirements, expected behavior, user needs, test plans, test coverage and alike are also identified and raised to the SW teams for them to take action
  • Comparison with a human-driven baseline and rollout recommendations.

• Plan and coordinate. Define tasks, dependencies, agent responsibilities, outputs, completion criteria and human approval gates. An orchestrator routes work, tracks progress and manages changes. Develop test plans alongside requirements and designs.
• Provide context and tools. Use existing team requirements, designs, engineering decisions, code examples, standards and known defects. Give agents controlled access to repositories, build tools, automated checks and test environments. Log actions. Shared, versioned memory retains artifacts, decisions and evidence; separate drafts from approved content.
• Iterate and check. Each stage follows: read inputs → produce → check → revise → human approval → handover. Agents assess results and adapt using compilation, static analysis, tests and peer feedback. Set iteration, time and cost limits. Escalate ambiguity, repeated failures and decisions outside agent authority.
• Communicate and trace. Handovers include versions, requirement references, check results, open questions and approval status. Return failures to the responsible stage and recheck affected downstream work. Changes to requirements or acceptance criteria require human approval.
• Keep humans accountable. Humans define needs, use cases, behavior, design constraints, coding standards, guardrails, testing goals and quality expectations. They approve decisions and handovers, resolve ambiguity, and own verification, validation and acceptance.
• Test adverse conditions. Cover normal operation, boundaries, invalid inputs, dependency failures and recovery. Base acceptance checks on approved expectations; define selected checks independently of the coding agent.

Demonstrate maintainable software that meets acceptance criteria. Measure delivery time, human effort, rework, defects, requirement coverage, agent retries, interventions, costs and recovery from changes. The final recommendation must identify what to scale, what to improve and where human expertise remains essential.