Migrating AI Context: 7 Principles
Migrate AI agent context by selecting verified facts and extracting information task-by-task to maintain reliable LLM performance
A good AI context migration from your current LLM doesn't mean taking everything over. Instead, it means strategically selecting the information and routines that keep your agent working reliably.
The 7 Principles for Successful Context Migration
1. Completeness isn't a quality measure here
An agent with 200 facts, 30 of which are outdated, performs worse than an agent with 40 verified facts.
Here's why: False facts are applied with the same confidence as correct ones. The error often only becomes apparent when the result is already wrong.
Better to have fewer pieces of information, as long as they're verified and reliable.
2. A chat history is a protocol, not a knowledge base
A chat history contains abandoned ideas, corrections, misunderstandings, and old numbers - often sitting right next to information that's still relevant.
The person who wrote the history usually knows what's still valid. An agent reading the history later doesn't.
That's why a chat history isn't automatically a suitable source for your agent's long-term context.
3. A longer prompt doesn't expand your available data
No matter what you ask your LLM to extract: the model can only access its stored memory and the conversations available to it.
A sophisticated extraction prompt can better structure this material. But it doesn't create additional information.
So more prompt doesn't mean more source material.
4. Small units make extraction easier
Extract information for one recurring task at a time, rather than pulling everything from existing conversations all at once.
This keeps extraction manageable and makes results easier to verify. Plus, errors are easier to trace back to a specific document instead of getting lost somewhere in 40 pages of content.
So migrate strategically, not everything in one go.
5. Verification is the critical quality step
The handoff document was created by a model summarizing itself. Once this document becomes the authoritative source for your agent, any errors remain invisible: there's nothing left to compare the result against.
That's why verification matters so much.
Ten minutes of reading per document makes the difference between a good migration and one that looks plausible but is actually poor.
6. Don't transfer anything your target system already knows
Customer lists, project status, due dates, or who's working on what: awork already contains this information live.
If we copy it into the agent context as well, we end up with two sources for the same information. These immediately start diverging and errors often go unnoticed.
Remove such information from the migration and let your agent read the real, up-to-date data.
7. It's not the history you're missing, it's the routines
The feeling of "starting from scratch" doesn't come from missing chat histories. It mainly comes up when recurring tasks suddenly need to be explained again.
That's why it's often enough to migrate just the five to ten tasks that someone actually repeats regularly.
This makes the fresh start feeling disappear, without having to take over your entire archive.
What Can't Be Transferred
Over months, you develop how your agent should work: what tone it uses, how much pushback you want, and when the agent should ask questions instead of just diving in.
This fine-tuning can't be fully transferred. Especially at the start, you'll probably need to make some corrections.
Tip: Write down the corrections you keep making repeatedly. Whatever proves to be consistently useful, you can then add to your AI context weekly.
After about a month, this process should be complete.
The Test for Successful Migration
Take five real tasks where you still have good results from your old system.
Run those same tasks in the new system and compare the results.
If the results hold up, the migration was successful, regardless of how little you actually transferred.
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