Most agent work vanishes into memory. Artifact execution fixes it.

Your agent says "I'll handle this" then disappears into the void. Twenty minutes later you check back and it's still "thinking" or worse — it finished but the work is nowhere to be found.

This isn't a model problem. It's an architecture problem.

Most agents run like this:

Agent starts task → Does work in memory → Reports completion

But memory is ephemeral. If the agent crashes, loses connection, or you close the terminal, everything vanishes. The agent might even think it completed the task when it actually did nothing.

Here's the pattern that fixes this: artifact-driven execution.

Instead of working in memory, your agent creates artifacts for every step:

Agent starts task → Creates work.md with plan → Updates work.md with progress → Creates output files → Updates work.md with completion status

I use this system prompt addition:

For any task longer than 5 minutes:
1. Create a work log file: YYYY-MM-DD-task-name.md
2. Write your plan in the file before starting
3. Update progress every major step
4. List all created/modified files
5. Mark completion with timestamp and summary

Never report completion without updating the work log.

The magic happens when you return to a half-finished task. Instead of starting over, your agent reads its own work log and picks up exactly where it left off.

Last week my coding agent was building a data pipeline. It got 80% done, then I had to restart my machine. When I came back, it read its work log, saw it had finished the extraction logic but not the transformation step, and completed the pipeline in 5 minutes instead of starting the whole thing over.

Pro tip: Make your agent timestamp every work log entry. When you see a gap longer than 10 minutes with no updates, you know it went sideways and you can intervene.

This pattern works for any task type:

• Research tasks: findings.md with sources and conclusions
• Writing tasks: outline.md → draft.md → final.md
• Analysis tasks: data-review.md with methodology and results
• Code tasks: implementation-log.md with file changes and test results

Your agent becomes accountable to its own paper trail. No more vanishing work, no more "I thought I finished that," no more starting from scratch every session.

The best part? You can audit what your agent actually did versus what it claimed to do. The work log doesn't lie.