Architecture

AI Sales Agent architecture — memory & compliance

A real estate sale isn’t a chat.

It’s hundreds of parallel relationships — weeks long, across channels, with hard compliance to honor. Most AI tools break on at least one of these.

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The three hard problems

Three things break most real-estate AI

01

Memory

“A hot lead replies after three weeks. The AI: ‘Who is this?’”
Persistent · across sessions
02

Scale

“200 leads tonight. You reach 5. The hot ones go cold while you sleep.”
1 persona · ×N lead agents
03

Compliance

“The AI texts a do-not-contact lead. Your broker calls before lunch.”
Enforced before every action
The architecture

Here’s how the architecture answers all three

Three layers, evaluated top to bottom on every action — compliance first, always.

L0Complianceruns first · immutable

Every action is checked before it leaves the system. If a guardrail trips, nothing ships — no exceptions, no overrides.

DNCopt-outidentitylanguagescopeframing
L1Persona

Who the agent is. Anna · Lily · Lucas · +custom — a complete, configurable behavior profile.

configurable identitysegment focuspersonal styleautonomyescalationcadence
L2Agent

Two cooperating instances — two parallel conversation surfaces, wired to one shared core.

Coordinator agenttalks to the realtor · ×1
IN directives, questions, preferences
OUT briefs, alerts, confirmations
Lead agenttalks to each lead · ×N
IN messages, activity, replies
OUT responses, schedules, CRM updates
1. PerceiveA signal arrives — message, activity, or scheduled trigger.
2. UnderstandPulls relevant context — your style, the lead, the market.
3. DecideApplies the playbook — segment, autonomy, escalation.
4. ActExecutes — sends, schedules, tags, escalates, hands off.

↻ The loop runs on every signal — message, activity, or scheduled trigger.

Playbooks

Decision logic you control

Segment routing

Your taxonomy — buyer, seller, luxury, FSBO, anything.

Intent detection

Graded signals — high · moderate · low.

Escalation triggers

Pricing · offers · contracts · channel failure.

Handoff protocol

Clean AI ↔ human transitions, run as a state machine.

Memory & learning

It remembers — and it improves

Realtor profile ×1

Your tone, scripts, working hours, focus.

Lead memory ×N

Facts, preferences, deal-breakers, deadlines.

Activity history append

Every touch, reply, escalation, handoff.

Learning loop
Evaluatorscores outcomes
Refinertunes playbooks
Re-injectpersona & playbooks
Persona design space

One architecture. Many distinct agents.

Each persona is a complete behavior profile — different segment, different pace, different judgment thresholds. Different agents in practice, sharing the same architecture underneath.

A
AnnaGeneral / dual
  • ToneWarm
  • AutonomyPropose
  • EscalationStandard
  • CadenceDaily
L
LilySeller-focused
  • ToneConsultative
  • AutonomyPropose
  • EscalationFast
  • CadenceDaily
L
LucasBuyer-focused
  • ToneDirect
  • AutonomyHigh
  • EscalationOn intent
  • CadenceReactive
+
CustomBuild your own
  • ToneConfigure
  • AutonomyTune
  • EscalationTune
  • CadenceTune

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