The Memory Problem
Agents That Actually Remember
The #1 frustration with AI agents is memory loss. Every conversation starts from zero. Users repeat themselves endlessly. Agents never learn. We solved it.
Memory isn't just stored, it's processed. A Temporal workflow clusters related memories, summarizes them, merges duplicates, and promotes important facts to long-term storage. Agents get smarter over time.
Memories are scoped to instance (company knowledge), app (domain expertise), agent (personality), and session (conversation). RollCall's Lisa remembers editing preferences. PipeScout's Drew remembers account context.
Memories lose importance over time (natural forgetting), but get boosted when recalled or validated. Frequently useful memories survive. Irrelevant ones fade. Like how human memory actually works.
checkMemoryHealth() surfaces memory bloat, stale clusters, and scope imbalances. Agents don't just remember -- the system monitors the quality of what they remember.
| ChatGPT / Claude | LangChain / CrewAI | steadybase | |
|---|---|---|---|
| Session memory | Yes | Yes | Yes |
| Cross-session | Limited | Manual | Automatic |
| Consolidation | -- | -- | Yes |
| Entity scoping | -- | -- | 4 scopes |
| Importance decay | -- | -- | Yes |
| Health monitoring | -- | -- | Yes |
Why 95% Fail
Three failure modes. One architecture.
Harvard research shows 95% of AI projects fail. Not because the models are bad -- because there's no foundation underneath.
Tiered memory system + Temporal consolidation
Agents forget everything between sessions. Our 5-tier memory architecture with importance decay and entity scoping means agents learn and remember.
Agent Framework + Temporal orchestration
Agents operate in silos. Our Signal Bus, inter-agent messaging, and durable Temporal workflows enable real coordination across apps.
Tenant system + cost tracking + review gates
Agents spend money and take actions with zero oversight. Our per-agent cost tracking, approval queues, and audit trails provide institutional governance.
Five Pillars
Everything agents need to run in production
Agents that actually remember. A 5-tier memory architecture with Temporal-powered consolidation, entity-scoped storage, and importance decay that lets agents get smarter over time -- not just store more data.
Durable workflow execution with crash recovery, automatic retries, and typed inter-agent messaging. Agents coordinate through a shared Signal Bus with priority levels and TTL.
Real-time signal detection across 10+ sources, company and contact enrichment, AI visibility monitoring, and autonomous support scanning. Intelligence feeds directly into agent decision-making.
Per-agent cost tracking, human approval gates for high-stakes decisions, 4-level credential resolution, and full audit trails. Agents operate within governance structures, not around them.
PostgreSQL with row-level security for tenant isolation, AWS Cognito authentication, declarative app manifests, and a plug-in architecture where new apps install like new businesses opening in a city.
Architecture
The full stack, visualized
5
Apps
9+
Agents
5
Memory Tiers
14
Workflows
50+
Tables
242
API Routes
Ready to see it in action?
This is the foundation that every AI agent needs to run in production. Coordination, governance, and infrastructure that actually works.