Platform

Memory is a first-class platform primitive.

Five memory layers give agents context without turning every task into a retrieval problem. A memory gate decides whether retrieval is needed at all.

Working memory

Current task state, plan, artifacts and active subtasks.

Semantic memory

Durable facts and learned knowledge.

Episodic memory

Past experiences, actions, outcomes, failures and corrections.

Procedural memory

Reusable how-to knowledge, represented as skills.

Organizational memory

Approval chains, handoffs, recurring processes and exception patterns.

Memory gate

Can current context handle the task?
        │
       Yes → Continue
        │
        No
        ↓
What knowledge is missing?
        ↓
Retrieve the correct memory /
knowledge / source of truth

Reduces noise, latency, token usage and stale-memory risk.

Model strategy

One strong shared model, many specialists.

The model layer stays replaceable — local, on-prem or cloud. Specialization comes from personas, skills, tools and policies rather than from separate deployments.

Shared Base Model
   ├── Accountant
   ├── QA Engineer
   ├── Developer
   ├── Researcher
   └── Personal Assistant

Trace / experience ledger

Every run is structured history.

Learning requires an auditable execution record. Runs are reproducible from versioned references.

RunStartedGoalReceivedPlanCreatedAgentResolvedMemoryDecisionMadeMemoryRetrievedSkillRetrievedModelInvokedToolAuthorizedToolExecutedArtifactCreatedSubtaskDelegatedHumanApprovalRequestedReviewerEvaluatedTaskSucceededTaskFailedRunCompletedLearningCandidatePromoted