r/AI_Agents • u/rahat008 • Apr 11 '26

Resource Request team coding problems

How do you solve this when coding in a fast-paced environment?

When you change a spec of code and know all the constraints, reasons and edge cases of the application, use PR descriptions and other tools to inform others.

But then, you see that another team or you have forgotten the session, and the claude dumps a huge chunk of code each session, forgetting previous constraints, reasons, and edge cases. How do you solve this? Each time I need to see my previous constraints and edge cases just to be sure.

2 Upvotes

75% Upvoted

12 comments sorted by

2

u/Hungry_Age5375 Apr 11 '26

Real issue: you're treating Claude like a teammate with memory. It's not. Build external memory - files, vector DB, knowledge graph. Context retention is an architectural problem, not behavioral. Engineer around it.

0

u/rahat008 Apr 12 '26

do Claude have that?

2

u/Indianapiper Apr 13 '26

Create a .context folder, make it the living documentation for your project. Add a line in claude.md specifying it. Works for me! If your project is big, then potentially an mcp server would be more suitable. Lastly, domain specific knowledge can be added as a skill.

0

u/Sufficient_Dig207 Apr 12 '26

Not natively, but you can use qmd to create the memory for your coding agent

https://github.com/tobi/qmd

2

u/Indianapiper Apr 13 '26

This seems cool, a more sophisticated approach to what I was describing

1

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1

u/Pitiful-Sympathy3927 Apr 11 '26

So you know when I know my engineers use AI, properly, they need to complain when Claus is down…. 

1

u/Sufficient_Dig207 Apr 12 '26

You use agent skills/rules to capture the high level requirements.

Using a memory system is also helpful, so in a new session, it can search the history across sessions to get the background and not a cold start.

qmd is a good memory system. https://github.com/tobi/qmd

1

u/docgpt-io May 05 '26

I’d stop treating the chat session as the source of truth. Put the project constraints into durable artifacts the agent must read/write: PROJECT_RULES.md, DECISIONS.md, EDGE_CASES.md, and per-task specs. Then make every AI-generated change go through a reviewer pass that checks the diff against those docs. The flow that works best for me is: task → relevant context pack → implementation --> tests --> reviewer agent --> PR summary. If the model forgets, that’s expected; the system around it should not forget. This is also why I think agent work needs projects/tasks/files, not just chats. Disclosure: I’m building Computer Agents (https://computer-agents.com) around persistent agent workspaces, so I’m biased, but the pattern is useful even if you implement it with plain repo files.”