Context Engineering: Managing AI-Generated Code Complexity (www.innoq.com)

AI-assisted coding can dramatically increase the volume of code produced, but that gain often just moves the bottleneck to human review and to LLM failures caused by bloated context. The piece argues that large system contexts — many files, error logs, or long conversational histories — degrade model performance (“context rot”) and make agents behave erratically. Agentic programming helps by fetching targeted code context, but it can also introduce noise. The author demonstrates practical fixes (git add -p, git stash -u, git diff --staged, stash pop) and workflow discipline: break work into small, reviewable features, do sequential agent sessions, use sub-agents or session summaries to limit context, and favor test-driven prompts and plan modes (e.g., Claude Code, Codex) to give agents clearer, smaller tasks. This matters because preserving AI productivity requires changing how teams commit and review code: more frequent, smaller commits and WIP checkpoints make changes comprehensible to reviewers and easier to roll back. Technical implications include respecting LLM context window limits, treating the session history as persistent system context (so “course-correcting” is often ineffective), and using tactical session resets instead of incremental fixes when an agent goes off-track. The takeaway: “context engineering” is less buzzword and more a practical discipline—limit scope, keep context minimal, iterate with tests and small commits to retain genuine productivity gains from AI.