Thesis: Context Engineering for Autonomous Software Development: Bridging the Model Capability Gap
Knightec Group ABOm tjänsten
High Level Description
Autonomous AI coding agents increasingly rely on powerful frontier language models to understand and modify complex software
systems. However, these models can be expensive to operate, while smaller and more affordable models often struggle with the same
development tasks.
Context engineering offers a potential way to narrow this gap by systematically selecting, structuring, and supplying relevant
information about the codebase, architecture, development conventions, and task at hand.
This thesis investigates whether well-engineered context can enable smaller or less capable language models to approach the
performance of frontier models on realistic software development tasks. It also explores how much context is actually needed, and
whether providing too much information can reduce performance rather than improve it.
Project Description
In this thesis, investigate and build a reusable context engineering pipeline for autonomous AI coding agents. The pipeline should
support different strategies for selecting, structuring, and supplying context to language models working on software development
tasks within an existing codebase.
Three context engineering approaches should be compared:
Persistent project context: Human-authored documentation, architecture descriptions, coding guidelines, and project instructions
that remain available across tasks.
Task-specific manual context: Relevant code, dependencies, and architectural information selected and prepared by a human for a
particular development task.
Automatically generated context: Relevant information identified and assembled automatically through repository analysis, code
search, retrieval, or summarization.
These approaches should be evaluated against a baseline coding agent operating without additional prepared context, while
retaining its normal ability to explore the repository and use development tools.
The evaluation should compare smaller and frontier language models performing the same tasks in representative industrial-style
software repositories. The solution should be model-agnostic, supporting both local and cloud-based inference. Local execution of
smaller models is optional, not a requirement.
The prototype should preferably be built on an existing autonomous coding agent harness, such as OpenCode, rather than requiring a
new agent framework.
Particular attention should be given to context quantity and relevance. By systematically varying the amount of useful information
and introducing redundant or unrelated context, investigate when additional context stops improving performance or begins to
negatively affect the agent's results.
The evaluation should use reproducible development tasks, such as feature implementation, bug fixing, and refactoring, with
automated tests and clearly defined success criteria. It should also distinguish between context prepared before task execution
and information the agent discovers independently through repository exploration.
Who are we looking for?
Purpose
In this thesis, investigate these questions:
The primary evaluation should measure functional task success and the reduction in the performance gap between smaller and
frontier models, without requiring a predefined equivalence threshold.
Experiments should use consistent agent tooling, reproducible repository states, and comparable execution conditions.
Out of scope: Training or fine-tuning language models, developing a new autonomous coding agent framework, extensive hardware
benchmarking, optimizing local inference infrastructure, and establishing universally optimal context configurations across all
software development environments.
References
- Anthropic, Effective Context Engineering for AI Agents —
https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents
An Exciting Journey with Knightec Group
Semcon and Knightec have joined forces as Knightec Group. Together, we are Northern Europe’s leading strategic partner in
product and digital service development. With a unique combination of cross-functional expertise and a holistic business
understanding, we help our clients realize their strategies – from idea to complete solution.
Practical Information
This is a thesis position, located at our office in Sundsvall. Start date January or March 2027.
Please submit your application as soon as possible, but no later than 2026-11-30. If you have any questions, you are welcome to
contact johanna.edstrom@knightec.se. Note that due to GDPR, we only accept applications through our careers page.