Active Memory · compounding

Memory is the moat.

Every AI company has the same models. What isn't commoditized is your organization's knowledge — the decisions, preferences, patterns, and insights that make your team effective. Cortex turns that implicit knowledge into a compounding asset.

memory · graduationL1 → L4
L1 · session92%
L2 · working71%
L3 · durable48%
L4 · permanent22%
[ 01 · The problem ]

AI agents have amnesia. Your organization pays the price.

Bolting on a vector database and calling it “memory” doesn't fix this. That's a filing cabinet. Real memory is living, self-curating, earned.

Most AI agents are stateless. Every conversation starts from zero. There's no accumulation of knowledge, no organizational learning, no way for what one agent learns to benefit another.

The tribal knowledge problem that plagues human teams gets replicated in AI teams — each agent is an island.

[ 02 · How Active Memory works ]

Three systems, running on every turn.

Capture, retrieve, score. The agent earns its memory through use — you never curate it by hand.
Auto-Capture

Extracts facts after every exchange.

No manual tagging

Cortex's fact extraction analyzes each turn and persists structured facts into the memory store. The agent learns by doing its job.

  • Decisions and commitments
  • Architecture and technical details
  • Identity and relationship info
  • Preferences and communication styles
  • Project context and milestones
  • Policies and procedures
QMD

Query → Memory → Deliver.

Runs before every LLM turn

The memory system actively fetches and ranks relevant facts, then injects them into the agent's context — on every response.

  • 01 · Query
    Searches the memory store for facts relevant to the current conversation.
  • 02 · Score
    Ranks results by relevance, usefulness history, and confidence.
  • 03 · Deliver
    Injects the most relevant facts into the agent's context for this turn.
Feedback

Keeps what works, drops what doesn't.

Usage-based scoring

After each response, keyword-overlap analysis compares injected facts against the final answer. Useful facts climb. Noise fades.

  • Facts that contributed to good responses get boosted
  • Facts that weren't relevant get demoted
  • Valuable knowledge surfaces more often. Noise fades naturally.
[ 03 · Graduation system ]

Four tiers. TTL by proven value.

Knowledge has to earn its way up. Volatile → Daily → Stable → Permanent. Only the facts that keep getting used survive the promotion gates.
Volatile
TTL · 1 hour

Just captured. Needs to prove it's worth keeping.

Daily
TTL · 24 hours

Showed early value. Getting a longer look.

Stable
TTL · 7 days

Consistently useful. Earning its place.

Permanent
TTL · Forever

Battle-tested knowledge. Part of the agent's core understanding.

Promotion thresholds
  • Volatile → Daily: score ≥ 0.4
  • Daily → Stable: score ≥ 0.6
  • Stable → Permanent: score ≥ 0.8, used 3+ times, active across 2+ days

score = 0.40 × usefulness + 0.30 × (access_count / injection_count) + 0.30 × confidence

[ 04 · Scopes ]

Agent. Team. Company.

What makes Cortex transformative is scoped memory at three organizational levels — individual memory is just the start.
Scope 01

Company scope

Set once. Shared everywhere.

Company policies, glossary, architecture decisions, org structure. Every agent in the company can access these.

Scope 02

Team scope

Shared by function.

Team-specific workflows, client assignments, domain conventions, runbooks. Agents on the same team share context automatically.

Example: A sales agent and a support agent on the same team both know a client's preferences without being told twice.
Scope 03

Agent scope

Personal to one teammate.

One agent's role, working style, current projects, and accumulated interaction context.

[ 04 · Knowledge compounding ]

The longer you use Cortex,
the more valuable it gets.

Most AI tools feel the same on day 1 and day 100. Cortex gets better every day. Drag the handle or hover a stage.
← → scrub · or hover a stage
[ 05 · vs. other approaches ]

Living memory vs. a filing cabinet.

Same capability, three approaches. See which ones actually compound.
CapabilityCortexTypical AI agentStatic RAG
Learns from conversationsAutomatic
Knowledge graduation4-tier system
Self-curationUsage-based scoringManual only
Organizational scopingAgent → Team → Company
Knowledge promotionConvergence detection
Audit trailsFullVaries
Gets better over timeCompoundingSame on day 1 and day 100Only if manually updated

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