Banks, funds and fintechs are on stage at @aiDotEngineer NYC this week, talking about agents in production. One question we'd add to every deployment checklist: When the agent is wrong, can you show what it remembered, where that came from, and who corrected it?

That's the layer we build: configurable agent memory your team can see into. What was stored, retrieved, corrected, and why. memory.atomicstrata.ai/?utm_…

@Google’s new @GeminiApp agent keeps four kinds of memory: session, semantic, procedural and episodic. Memory is now the headline feature of workplace agents. The next question every team will ask: can we see what it remembers, correct it, and take it with us?

1/ Most RAG setups read your documents at question time. LLM Wiki Compiler reads them first, and compiles them into a wiki your LLM can use. It works in two phases: 2/ Phase 1: concept extraction. It scans each changed source, pulls out its concepts, and finishes extraction across the whole corpus before writing a single page. Shared concepts merge consistently, and extraction failures surface before they touch retrieval. 3/ Phase 2: typed wiki pages. Each page combines synthesized prose with source citations and links to related concepts. Scattered references become connected knowledge. 4/ Every later query starts from that structure instead of raw chunks. Open source: github.com/atomicstrata/llm-… How it works: llmwiki.atomicstrata.ai/conc…

Quick question: where does your agent's memory live today?

Atomic Memory Cloud gives your team that view, hosted or self-hosted with the same SDK. Now in public beta: memory.atomicstrata.ai/?utm_…

Vast Data's CTO says agent memory belongs in storage. For the KV cache that makes agents fast, agreed. But there's a second layer: what your agent believes about users, projects and decisions. That one needs to be visible. What was stored, retrieved, corrected, and why.

Add durable memory to your AI agent in a few commands. The Atomic Memory Cloud OS QuickStart takes you from local setup to your first stored and retrieved preference with Docker, an OpenAI API key, and three CLI steps. Start building with a configurable AI memory: docs.atomicstrata.ai/open-so…

Imagine a @DBSbank AI assistant that goes beyond generic banking answers. Instead, it uses your approved financial context to give more personalized guidance on savings, cards, and travel benefits without making you explain your goals every time. Atomic Memory Cloud can help you achieve that. memory.atomicstrata.ai/?utm_…

Are you ready to reduce repeated context and lower token costs? Check out Atomic Memory Cloud: memory.atomicstrata.ai/?utm_…

So how does agent memory reduce that cost? Having an external memory system lets an agent extract durable information from each conversation and store it separately, rather than keeping everything in its active context. That information is indexed by meaning and task relevance. So the agent can then retrieve the specific facts it needs when a later task requires them. This gives the model a smaller, task-specific context to process, and doesn't require the agent to read the entire transcript again.

Cut your token costs by up to 90% with agent memory. Ever notice your agents reload the same files and repeat decisions? That's because context stays trapped in the transcript of every previous task. Each repeated file and decision adds more tokens for the model to process, which increases the cost of completing the task.

Fluency makes the question emotionally compelling, but useful behavior and subjective experience still need to be evaluated separately.

More agents only help when the work can be divided cleanly, otherwise coordination costs quietly become the main workload.

Autonomous systems need clear boundaries because a mistake involving external systems quickly becomes an organizational liability.

Having a configurable AI Memory is the key to this. 🔑

An AI safety framework needs auditable memory.

Congrats on the achievement! 👏

Agent safety has to be part of the runtime, since sandboxing determines which mistakes stay local and which ones become incidents.