Every time Claude Code compacts, you feel it. That sinking moment when you realize half your project context just evaporated. The agent that was making brilliant connections five minutes ago now needs everything re-explained.
Here’s the thing: the good news is that Claude Code now clears context at the end of planning mode to give you more room in execution mode. That helps.
But there’s a better approach that makes context loss nearly irrelevant. And it has nothing to do with bigger context windows or better prompts.
You Might Be Suffering from Context Dependency
Let me ask you a few questions:
- Do you dread the “compacting conversation” message because you know things are about to break?
- Have you ever re-explained the same project structure three times in one session?
- Do your multi-agent workflows mysteriously “forget” decisions made earlier?
- Are you constantly copying and pasting context from one conversation into another?
- Does your AI make confident statements that contradict what it said twenty minutes ago?
- Have you lost work because the context window couldn’t hold everything?
If you’re nodding along, you’re not alone. But you’re also making a mistake I made for months.
The Real Problem Isn’t Memory
Here’s what I used to believe: the solution to context loss was more context. Bigger windows. Better prompts. More detailed system instructions.
I was wrong.
The real problem is that we treat AI like it should “remember” things the way humans do. We expect continuity. We hope the important stuff persists. We cross our fingers during compaction.
But hoping isn’t architecture.
Think about it this way. When you have a team of people working on a project, you don’t rely on everyone’s memory to stay synchronized. You use documents. Specs. Decision logs. Handoff notes.
Why would an AI team be any different?
Think about the handoffs you already use with people. You write requirement documents. You write go-to-market plans. You create specs before development starts. You document decisions so new team members can get up to speed.
You already know how to do this. You’ve been doing it your entire career.
The hidden problem isn’t that context windows are too small. It’s that we forgot to apply what we already know about team coordination to our AI workflows.
The Fix: File-Based Handoffs
I have an architect agent that outputs its conclusions and explanations into a /decisions folder. Not into the context window. Into actual files.
The next agent, the tech lead, picks up those files. It does more planning, creates a spec, and drops it into that folder for the next agent.
The engineering agent grabs that file and goes to work. It leaves a summary of what and how it worked into a /dev folder.
The QA agent picks up those files to use as it creates tests.
See the pattern?
It’s exactly what you’d do with a human team. The architect doesn’t whisper decisions to the tech lead and hope they remember. They write it down. The tech lead doesn’t verbally brief the engineers and cross their fingers. They create a spec.
Each agent has a defined input (files from the previous stage) and a defined output (files for the next stage). The context window becomes a workspace, not a warehouse. It doesn’t need to hold everything because everything important is already externalized.
When compaction happens, nothing is lost. The files are still there. The next agent picks up exactly where the last one left off.
How to Implement This
Start by identifying your agent chain. What agents do you run in sequence? What decisions does each one make?
Then create the handoff points. For each agent, ask: What does this agent need to know from the previous stage? What does the next agent need to know from this stage?
Here’s a simple structure:
/decisions, Architecture choices, rationale, constraints. The “why” behind the big calls.
/specs, Technical specifications, requirements, acceptance criteria. The “what” that needs to be built.
/dev, Implementation notes, patterns used, known issues. The “how” it was actually built.
Each folder becomes a handoff point. Each file becomes a contract between agents.
The format matters less than the discipline. Markdown works fine. JSON if you need structured data. The point is that the intelligence leaves the context window and enters the file system.
The Principle
Here’s what I’ve learned after running multi-agent systems for months:
Context doesn’t need to survive in memory if it’s deliberately exported to files.
This approach eliminates many of the challenges that arise when you’re just hoping that context will be enough to keep everything moving intelligently.
The intelligence doesn’t live in the model. It doesn’t live in the context window. It lives in the handoff architecture, in the files that capture decisions and pass them forward.
When you build this way, compaction becomes a non-event. Clear the context? Fine. The files are still there. The next agent knows exactly what it needs to know.
You’ve been coordinating human teams with documents for years. Start coordinating your AI teams the same way.
Stop hoping your AI will remember. Build the architecture that makes remembering unnecessary.