Context Rot in AI Agents: Why Quality Degrades

Anyone who works with Claude Code, Cursor, or Copilot knows the pattern. The problem has a name: Context Rot. The first results are sharp and the architecture holds up. Then quality drops off. After 20, 30 minutes, the answers start repeating themselves. The AI agent loses context. It produces code that contradicts what it wrote five prompts ago.

Not a user error – an architecture problem. The solution lies in context engineering: the deliberate management of context windows through multi-agent orchestration. These are exactly the kinds of real-world problems I address in my AI & Automation consulting.

Context Rot: The 200,000-Token Problem

Every large language model has a context window. For Claude, it is 200,000 tokens. That sounds like a lot. But here is what happens in a typical development session: the agent reads files, analyzes code, writes solutions, gets feedback, reads more files. After half an hour, the context window is 70-80% full. From that point on, quality drops measurably.

Think of it like an overloaded workbench: when all the tools, sketches, and manuals are on the table at the same time, the craftsman cannot find anything. Focus suffers, mistakes creep in.

Context Rot describes the quality degradation of AI agents when the context window fills up with old, irrelevant content. Outputs become less accurate, contradictory, and code starts repeating itself.

The typical reaction: start a new session, manually rebuild the context, hope it lasts longer this time. That works for small scripts. For a project with 50+ files and complex dependencies, it is a waste of time.

GSD (Get Shit Done): Multi-Agent Orchestration Instead of a Solo Agent

GSD (Get Shit Done) solves this problem with an approach I know from my work with multi-agent orchestration: instead of a single agent doing everything alone, GSD uses specialized agents with separate context windows.

The open-source framework currently has 45,700 GitHub stars and is released under the MIT license. Installation is a single command:

npx get-shit-done-cc@latest

GSD describes itself as a “Meta-Prompting, Context Engineering and Spec-Driven Development System.” Built primarily for Claude Code, but also compatible with OpenCode, Gemini CLI, Codex, Cursor, and Windsurf.

The core idea: every agent gets a fresh 200k-token context window. Instead of stagnant water where the context grows murky, fresh water flows through each work step. A Researcher reads and analyzes, a Planner breaks down tasks, an Executor writes code, a Verifier checks the results. Each one with full context for its specific job.

GSD vs. a Traditional AI Agent: Spec-Driven Development

The critical difference is the second row of the table: Spec-Driven Development. At project start, GSD generates spec documents (PROJECT.md, REQUIREMENTS.md, ROADMAP.md, STATE.md) that serve as a blueprint for all subsequent agents. It works like a technical drawing instead of a verbal instruction: the drawing stays precise, no matter who reads it.

GSD Workflow: How Project Orchestration Works

The workflow follows six phases, controlled via slash commands:

/gsd:new-project     → Initialize project, generate specs
/gsd:discuss-phase   → Clarify requirements, define scope
/gsd:plan-phase      → Derive tasks, identify dependencies
/gsd:execute-phase   → Write code, parallelize where possible
/gsd:verify-work     → Check results, run tests
/gsd:ship            → Finalize, document

The Execute phase is where the architecture really shows its strength: GSD identifies dependencies between tasks and runs independent tasks in parallel – so-called Wave Execution. Two features that do not touch the same files are built simultaneously. Each task ends with an atomic Git commit. No massive “WIP” commit at the end of the day, but traceable, individual changes.

Who Uses GSD Today

According to the project’s data, engineers at Amazon, Google, Shopify, and Webflow use the framework in their daily work. The community is growing fast: 45,700 stars on GitHub speak for themselves.

In my own projects, such as AI adoption in mid-sized companies, I have confirmed the effect: once a project spans more than a dozen files, the multi-agent approach makes a noticeable difference. Any single response does not get better, but quality stays stable across the entire session. This matches my experience with multi-agent orchestration using OpenClaw: Agents that verify each other deliver more consistent results than a single agent doing everything alone.

The project’s motto fits: “No enterprise roleplay. Just build cool stuff consistently.” No overhead, no ceremony. Specs in, code out, verified and committed.

Limits of GSD: When the Framework Does Not Help

No tool without limitations. GSD requires CLI-based workflows. Anyone who develops exclusively in an IDE with a graphical interface will need to adjust. The spec files (PROJECT.md, REQUIREMENTS.md) need to be filled in upfront. For an existing project, that takes 15-30 minutes, but it pays off quickly.

Also: GSD orchestrates the work of AI agents – it does not make a bad model better. Working with a weak base model still produces weak results with GSD, just more cleanly divided. The biggest impact shows up with strong models like Claude Opus or Sonnet.

That is exactly how I use AI tools in my consulting projects as well: as a practical tool, not as an end in itself.

Who GSD Is For

GSD is not a tool for every use case. If you want to write a single Python script, you do not need multi-agent orchestration. The strength shows up in:

• Projects with more than 10 files, where a single agent’s context is not enough
• Teams that use AI agents systematically and need reproducible results
• Long-lived codebases, where consistent architectural decisions across many sessions matter
• Developers already working with Claude Code or similar tools who are hitting the limits of the solo-agent approach

If you are interested in using AI in software development more broadly, my post Website relaunch with AI instead of an agency provides a concrete, practical example of what AI-assisted development can achieve today.

Frequently Asked Questions About Context Rot and Context Engineering

What is Context Rot in AI coding agents?

Context Rot refers to the quality degradation that occurs when an AI agent’s context window fills up with old, irrelevant content. After 20-30 minutes of intensive work, output quality drops measurably: answers become less accurate and contradictory, code blocks repeat themselves. The problem affects all LLM-based coding tools, whether Claude Code, Cursor, Copilot, or others.

What does Context Engineering mean?

Context Engineering is the deliberate control of what information an AI agent has in its context window at any given point in time. Instead of packing all information into a single context window, Context Engineering distributes work across multiple specialized agents, each with fresh context. GSD is an open-source framework that implements this approach.

How is GSD different from normal prompting?

Normal prompting works within a single context window. GSD uses Spec-Driven Development: structured documents (PROJECT.md, REQUIREMENTS.md, ROADMAP.md) replace verbal prompts. Four specialized agents (Researcher, Planner, Executor, Verifier) each work with their own 200k-token context window. The result: consistent quality even in complex projects.

Does GSD only work with Claude Code?

No. GSD was built primarily for Claude Code, but it is also compatible with OpenCode, Gemini CLI, Codex, Cursor, and Windsurf. The best results show up with strong models like Claude Opus or Sonnet.

Next Step

You are using AI agents in development and hitting the limits of Context Rot? I consult on multi-agent architectures, context engineering, and the productive use of AI tools in real projects.

→ Schedule an initial call – free of charge

→ Or read more first: AI Workshop: Business Processes

Tools: GSD (Get Shit Done) · Claude Code · OpenCode