Your AI Context Is Perishable Unless You Standardize

Forty-seven prompts refined. Three modes configured. One PR merged.

Context: gone.

The evaporation problem

You spend an hour teaching your AI coding agent about your codebase. You refine the prompts, configure modes for your specific workflows, build up a rich task history that makes the agent actually useful. Then you commit your code, close the task, and move on.

Next week, a teammate picks up that same file. They start from scratch. All your learnings, your prompt refinements, your mode configurations - invisible. The code shipped. The context evaporated.

JB Brown at Smartsheet has a name for this pattern.

"I would characterize our efforts as like perishable single engineer work. So I use Roo, prompt it for some stuff... but all of the context and all of the learnings that I put into the prompt history: all that stuff is gone."

JB Brown, Roo Cast S01E19

The code survives. The context does not.

The shift to team-based context

Smartsheet's solution: stop treating AI context as individual and start treating it as team infrastructure.

The pattern has three parts.

Shared directory conventions. Documentation and examples live in predictable locations. When the agent indexes the codebase, it finds the same context regardless of which developer is running the task.

Multi-tenanted memory banks. Developers can share task context without overwriting each other. No more "my learnings clobbered your learnings" when two people work on the same repo.

"We've multi-tenanted that so that developers can share a task and not have: get collapse. You know when you check that in have it sort of overwrite and have a clash of two different recent tasks overwriting another."

JB Brown, Roo Cast S01E19

Standard modes per repository. The same set of modes exists in every repo. When an engineer picks up unfamiliar code, they inherit the team's accumulated AI context instead of starting from scratch.

"We're now moving to team-based where teams have standards, practices, paradigms that they all use the same way and then they get to benefit from each other from their efforts of the context that they build up and the modes that they build that are specific for that codebase."

JB Brown, Roo Cast S01E19

The tradeoff

This requires upfront coordination. Someone has to define the directory conventions. Someone has to build the shared modes. Someone has to document what goes where.

For a single developer on a side project, this is overhead. For a team shipping production code together, it's the difference between compounding returns and constant restarts.

The pattern also requires discipline. If developers keep their best prompts local, the shared context stays shallow. The benefit only compounds when the team actually contributes to it.

How Roo Code preserves team context

Roo Code closes the loop on context preservation through its mode and memory bank architecture. With BYOK (bring your own key), teams control their AI infrastructure while building shared context that persists across sessions and developers.

Here's how it works: modes defined in .roomodes files live in version control alongside your code. Memory banks store task-specific learnings in a structured format that can be scoped per developer or shared across the team. When a new developer opens the codebase, Roo Code automatically loads the team's accumulated context - the modes, the patterns, the institutional knowledge baked into the configuration.

The key insight: context that lives in the repo compounds; context that lives in task history expires.

Individual vs. team-based AI context

Why this matters for your team

For a five-person engineering team, every handoff is a context reset. Developer A figures out how to prompt the agent to handle your legacy auth system. Developer B picks up a ticket in the same codebase and starts from zero. Multiply this across every codebase, every sprint, every new team member.

The compounding loss is invisible because no one tracks "time spent re-teaching the AI what the last person already knew."

Standard modes and shared memory banks turn that loss into cumulative gain. When developer B opens the codebase, they inherit developer A's learnings. The context lives in the repo, not in a closed task history.

The first step

Audit where your AI context currently lives. Is it in individual prompt histories? Local mode configurations? Files that never get committed?

Move one piece of context to a shared location this week: a mode definition, a documentation file the agent indexes, a memory bank entry. See if the next developer who touches that code benefits.

Context that lives in the repo compounds. Context that lives in task history expires.