AI Skill Report Card
Federating Distributed Memory
Federating Distributed Memory
Design memory federation systems that let distributed agents/nodes share, synchronize, and preserve memory reliably over time.
Quick Start15 / 15
Given a request, produce exactly three sections:
Recommendation▾
Add a second example with a contrasting scenario (e.g., strong consistency requirement or large-scale node federation) to show variability in output
Memory Architecture
[topology, storage tiers, ownership model, access patterns]
Sync Strategy
[propagation method, consistency model, conflict resolution, frequency/triggers]
Retention Policy
[retention tiers, TTL rules, archival/compaction, deletion/legal hold rules]
Example invocation → output shape:
**Input:** "Design memory federation for 5 collaborative agents sharing task context in real-time."
**Output:**
Memory Architecture
- Hub-and-spoke: central shared memory store (Redis/vector DB) + local per-agent working memory
- Ownership: each agent owns writes to its own namespace; reads are federated across all namespaces
- Storage tiers: hot (in-memory, <1hr context), warm (disk-backed, session-scoped), cold (archived transcripts)
Sync Strategy
- Push-based sync on write, with 500ms debounce batching
- Consistency model: eventual consistency, last-writer-wins with vector clocks for conflict detection
- Conflict resolution: semantic merge for append-only logs; manual flag for contradictory state updates
- Sync triggers: on write, on agent handoff, every 30s heartbeat
Retention Policy
- Hot tier: 1 hour TTL, auto-evict to warm
- Warm tier: 30 days, session-scoped, queryable
- Cold tier: indefinite, compressed + summarized, append-only archive
- Deletion: honor explicit purge requests immediately across all tiers; no silent deletion otherwise
Workflow13 / 15
Progress checklist for each request:
- Identify scale (number of agents/nodes) and topology constraints
- Determine consistency requirements (strong vs eventual) based on use case criticality
- Define memory tiers (hot/warm/cold) and what lives in each
- Choose sync mechanism (push, pull, hybrid) and conflict resolution strategy
- Define retention windows per tier and archival/compaction rules
- Output the three-section format: Memory Architecture, Sync Strategy, Retention Policy
Recommendation▾
Include a brief troubleshooting/edge-case section for network partitions or split-brain scenarios
Best Practices
- Default to eventual consistency unless the use case explicitly requires strong consistency (e.g., financial state, safety-critical coordination) — federation at scale rarely tolerates synchronous locks.
- Always separate working memory (fast, ephemeral, per-agent) from shared memory (federated, slower, authoritative).
- Use vector clocks or logical timestamps, not wall-clock time, for conflict detection across nodes.
- Prefer append-only logs with periodic compaction over in-place mutation for auditability and easier recovery.
- Tie retention policy to actual query/access patterns — don't archive too aggressively if agents need recent history for reasoning.
- Always specify what happens on conflict (merge, flag, last-writer-wins) — never leave this undefined.
- Include a deletion/purge path even in early designs — retroactive compliance is expensive.
Common Pitfalls
- Don't propose a single flat memory store for more than a couple of agents — always tier it (hot/warm/cold).
- Don't default to strong consistency/global locks "to be safe" — this kills scalability and is rarely justified.
- Don't ignore conflict resolution — silence on this is the most common failure point in federated memory designs.
- Don't conflate sync frequency with retention duration — they are independent decisions.
- Don't output prose paragraphs instead of the three required sections; keep the format scannable and structured.
- Don't recommend indefinite retention for all tiers by default — specify explicit TTLs and archival rules.