Write the problem hypothesis
For [user], the agent loses or retrieves [information] incorrectly across [scope], causing [wrong action or repeated work].
You can name one user, one situation and one measurable consequence without proposing a feature.This page is preserved so old links and saved research still work. Verify the original GitHub Issue before taking action, or choose a current signal from Opportunity Radar.
Browse current opportunities →[FEATURE] Memory write guards for multi-agent crews — prevent cross-agent memory poisoning
Evidence observed in crewAIInc/crewAI, a Agent Infrastructure project.
29 comments2 positive reactions113 days openProject Radar 95
feature-request
Browse current opportunitiesArchived brief · verify the original Issue
REPEATED ACROSS 2 PROJECTS
Related friction appears in 2 independent repositories. This is stronger than one backlog item, but still requires direct user validation.
Project strength and demand confidence are measured separately.SOURCE EVIDENCE
Read original GitHub Issue ↗Start with what users actually said
Reporter context: Feature Area Core functionality Is your feature request related to a an existing bug? Please link it here. NA - not related to an existing bug Describe the solution you'd like In multi-agent CrewAI crews, agents share memory (short-term, long-term, entity memory). A compromised or manipulated agent can poison shared…Excerpted from the public Issue. Read the complete thread before interpreting it.
Recruit: Recruit teams with live traces, tool calls or deployment constraints from a real agent workflow.
Guardrail: Test observable state transitions, permission boundaries and recovery from partial failure.
Interview five agent-platform engineers or technical operators
- Which information must persist?
- For how long and within what scope?
- Show the last wrong or missing recall.
- What data must never cross boundaries?
- How do you correct memory today?
Run the smallest experiment
Test one constrained memory scope with a small real dataset and a written expected-recall benchmark.
The system retrieves the right evidence across repeated sessions without leaking or inventing context.Test observable state transitions, permission boundaries and recovery from partial failure.Make a build decision
- Build: repeated pain and active commitment
- Narrow: pain is real but the audience or job differs
- Stop: weak frequency or no behavioral proof
Why this brief exists
Information has value only when it changes action. This page turns one public signal into a bounded validation exercise. It is a research aid, not proof of demand, investment advice or a product recommendation.