From the CoreCast
engineering team.

After 10 turns, most agents start forgetting — and users notice long before they can articulate why. Here's the architectural reason this happens, and what persistent memory actually requires to fix it.

Vector search answers "what's relevant?" Temporal recall answers "what happened recently, in order?" Most production agents need both — here's how to decide what to weight and when.

Treating your context window as memory works in demos and falls apart in production. Here's the precise architectural distinction that separates teams who get this right from teams who rebuild it twice.

The three architectural mistakes in our first implementation — synchronous indexing, flat memory model, no compaction — and how we fixed them without breaking existing customers.

Context stuffing feels cheap until your token bill lands. A clear-eyed look at the unit economics — and the three hidden costs beyond tokens — that make memory infrastructure the smarter long-term bet.

A coding agent with no memory is expensive autocomplete. Here's how to build one that remembers your architecture, your conventions, and your decisions — across sessions and across the whole team.

Three architectural mistakes — query-time isolation, flat scope model, missing retention controls — and the expensive lessons they taught us about building memory for SaaS products.

Support agents without memory make "I already told you this" complaints structurally worse. Here are the specific recall patterns — issue history, customer profiles, temporal queries — that move the needle.

Agent memory is personal data — and most teams discover what that means in their first enterprise sales call. Here are the engineering requirements GDPR and HIPAA actually create for your memory layer.

LangGraph handles execution state. CoreCast handles persistent memory. Here's exactly how they fit together — and why the combination covers what neither solves alone.