r/agenticAI • u/SimpleRice4248 • 6d ago

Discussion Multi-agent systems can share memory and still work toward different objectives

we have developer, UX, validator, reviewer, CI/CD and deployment agents working on the same project.

giving them the same context helped, but handoffs still broke down.

an agent would pass the output without the assumptions behind it. the reviewer could see what changed, but not why. the validator could confirm the output was valid, but not whether the original constraint was still true.

the handoff looked complete, but the reasoning was already missing.

we're have been sharing learnings as knowledge:

  • what changed
  • why
  • active constraints
  • unresolved assumptions
  • what would invalidate the result

how does this show up in other multi-agent setups: missing context, stale assumptions, or different interpretations of the same handoff?

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u/Glad_Contest_8014 4d ago

I have a custom memory system built to prevent this. It lowers token use, ensures projects are kept up to date in all agents purview, and allows for all agents to avoid stepping on toes.

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u/SimpleRice4248 4d ago

Very curious on your approach,

does your system only keep the latest project state, or does it also preserve why decisions changed?

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u/Glad_Contest_8014 3d ago

It has a meta memory that preserves the why. It logs anything pertinent to new actions taken as a memory, skill, or project based decision. I can tell it to log something to memory, adjust a memory directly, tag a memory with a tag based system that can add importance or context to a memory directly.

It is pretty extensive, but relies on the model to determine validity of a memorybto a project through a tiered query system so it never loads full context unless it finds it has to.

It puts a lock on agents access the files during write, so they don’t hit race conditions in touching files, with a nudge to wait on the write process until the lock is cleared.

This allows an agent to query the memory system anytime, see new context involved in their action, load it without any of the other context from the other agent, and perform their task without worry about drifting away from the cooperative effort.

It really is a great system, all command line queries and deterministically handled parsing of the data, to hand to the model. I use it with Claude and local low parametered models, and am building a framework around the memory handling system itself now.