Master thesis - Context Engineering Strategies for Agentic Development at Bosch Group

Lund, , Sweden -

Full Time

Start Date

Immediate

Expiry Date

10 Feb, 26

Salary

0.0

Posted On

12 Nov, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Computer Science, Software Engineering, AI, Machine Learning, Data Science, Generative AI, Large Language Models, Python Programming, APIs, Software Engineering Practices, Agent-Based Systems, Analytical Skills, Problem-Solving Skills, Empirical Research, Self-Driven, Collaboration

Industry

Software Development

Description

Company Description At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference. Bosch R&D Center Lund stands for modern development in cutting edge technology in the areas of connectivity, security, mobility solutions and AI. We are growing rapidly and looking for people to join us on our mission to become the Bosch Group’s 1st address for secure connected mobility solutions. We are working on a range of interesting projects, with a particular focus on software development for the automotive industry, electrical bicycles and Internet of Things. Job Description Problem statement As AI agents handle increasingly complex, long-running development tasks, a critical challenge has emerged: managing limited context windows across multiple agent sessions. In continuous development scenarios—where agents work on the same codebase over days or weeks—agents must maintain coherence, recall previous decisions, and avoid redundant work within strict token constraints. Current approaches often focus on isolated sessions or rely on pre-computed retrieval (RAG). However, optimal performance requires thoughtful strategies across the entire agent lifecycle: pre-session preparation, intra-session dynamic retrieval, and post-session persistence. At Bosch Lund, where we work extensively with multi-agent systems, understanding which context management strategies provide the best balance of continuity, performance, and efficiency is crucial for production-ready agentic systems. Proposed solution This thesis investigates context management techniques across all three lifecycle stages in continuous development scenarios. The goal is to evaluate and compare different approaches, allowing flexibility and discovery throughout the research. Pre-session strategies: Initialization approaches (project docs, previous summaries), selective vs. comprehensive loading, preparation overhead vs. effectiveness trade-offs. Intra-session strategies: Just-in-time retrieval (dynamic file loading, targeted fetching), context refresh mechanisms, navigation and discovery during execution. Post-session strategies: Summary generation (compression, selective preservation), memory extraction and persistence, formats enabling future continuity. Implementation and evaluation: Design and implement 2-3 strategies at each lifecycle stage Benchmark on realistic multi-session development tasks Measure continuity, quality, token efficiency, and coherence Analyze which combinations work best under different conditions Possible extensions (if time permits): Investigate sub-agent architectures for parallel context handling Explore hybrid strategies combining pre-computed and just-in-time approaches Analyze how findings generalize across different types of agentic tasks You will shape the research direction based on your discoveries and interests during the project. Qualifications In order to be successful in the project: Master student(s) in Computer Science, Software Engineering, AI/Machine Learning, or Data Science Passionate about Generative AI, large language models (LLMs) and surrounding technologies Experienced with Python programming and comfortable working with APIs Interested in software engineering practices and agent-based systems Analytical with strong problem-solving skills and an interest in empirical research Self-driven and able to work independently while collaborating with the team Curious about emerging AI technologies and eager to contribute to cutting-edge research Additional Information Supervisors: Samuel Peltomaa, Staffan Lindgren Scope: We encourage to have a team of 2 master thesis students working on the thesis.

Responsibilities

The thesis investigates context management techniques across all lifecycle stages in continuous development scenarios. The goal is to evaluate and compare different approaches to improve agentic systems.