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Memory
3 posts about Memory. Every summary links to the original.
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Red Hat OpenShift Virtualization memory management overview
The post overviews recent memory management evolutions in Red Hat OpenShift Virtualization, spanning from the Linux kernel to the orchestration planes. Historically, the platform uses a 1:1 memory request mapping, but data shows most VMs use only about 20% of allocated RAM, creating a delta that scales infrastructure with workload definitions rather than demand. Node-level optimizations include free page reporting (FPR), which asynchronously recovers pages freed by the guest OS without forcing allocation like ballooning, and KSM zero pages for guest-agnostic deduplication of zero-filled memory. At the cluster level, Multi-Gen LRU integrated with control groups enables swapping to fast secondary NVMe storage, and a PSI-aware descheduler proactively migrates VMs to relieve memory pressure across nodes. OpenShift Virtualization 4.22 provides an autopilot feature that automates configuration for generally available memory optimizations, while developer preview capabilities require manual opt-in activation by default.
Building a Multi-Channel AI Agent with Twilio Conversations and eve
Building a multi-channel AI agent that remembers customers across SMS and WhatsApp is challenging because most projects require a custom memory layer; Twilio Conversations and Vercel’s eve solve this by cleanly separating short-term and long-term memory. eve holds the per-conversation transcript, while Twilio’s Memory Store persists extracted facts across sessions using automatic observation extraction from Conversation Intelligence. The tutorial walks through provisioning an Orchestrator Configuration that stitches SMS and WhatsApp into one thread, wiring a dynamic instruction that merges both memory sources, and using ngrok for local webhook tunneling. The resulting agent remembers customer context across channels and sessions, allows model swapping in one line via the AI SDK, and extends cleanly to voice and RCS without bespoke persistence code. The full sample project is on GitHub at IObert/twilio-conversations-eve-agent, and the tutorial includes provisioning steps using curl, environment variable setup, and local testing with pnpm dev.
AI Agents Need Memory: Building Persistent Memory for Agentforce
Enterprise AI agents without persistent memory suffer from the “Groundhog Day” problem, repeatedly asking users to re-explain themselves. Agentforce Memory addresses this by giving agents persistent memory that retains user preferences, conversation history, and context across sessions. The team first established a shared terminology document to define concepts like semantic and episodic memory before architectural discussions could proceed. The architecture evolved incrementally, starting with conversation history injection and later adding semantic retrieval to find relevant past information regardless of recency. Memory retrieval uses keyword search during pre-orchestration to balance context quality with low latency, and moving memory into the Reasoner simplified configuration for administrators. Privacy and user trust were treated as architectural requirements from the start, enabling individual user controls over memory creation and management.
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