We're thrilled to announce that Engram is now generally available. Engram is our managed memory and context service, purpose-built to help agents orchestrate workflows, learn from experience, and anchor decisions to trusted knowledge. If you've been following our work on memory for agents — from the conceptual framing in The Limit in the Loop to the architectural deep dive — today is the day that we open our doors for you to start building with Engram.
Memory is infrastructure
Agents we build are supposed to compound in value over time. They should build up interactions, accumulate context, and get more useful the longer they run. In practice, however, this value compounding does not happen, and agent usage may even backfire, due to three failure modes:
Long-context degradation. Sending whole conversations back to models on every turn drives up latency and cost. More importantly, this can cause answer quality to drop in the middle of long inputs even with state-of-the-art context windows.
Messy raw data. User interactions are noisy and facts evolve over time. Piling raw events into a data store and asking an LLM to reconcile them at query time pushes the hardest part of the problem to the worst place for solving it.
