How It Works

How It Works (Step-by-Step)

Questera’s personalization engine employs a sophisticated multi-agent approach, structured around dedicated AI agents, each assigned specific customer-engagement goals such as onboarding, retention, upsell, or reactivation.

1. Specialized Goal-Based Agents

• Each agent specializes in achieving one critical marketing objective (e.g., onboarding, retention, upsell).

• They operate independently, continuously observing shared user data, user journeys, and campaign states in real-time.

2. Shared Memory & Collaborative Decisioning

• Agents access and act upon a shared context and memory store:

• Real-time user behavior streams

• Historical interactions (opens, clicks, conversions)

• User lifecycle stage and intent data

• This common state allows agents to make informed, locally optimized decisions instantly.

3. Dynamic Coordination (Scheduler Agent)

• A dedicated Scheduler Agent arbitrates decision-making when conflicts or resource constraints arise (e.g., only one email or push notification can be sent today).

• It prioritizes actions based on agent priority, user context, urgency, and potential impact—resolving contention swiftly.

4. Critic Agent: Monitoring & Optimization

• A Critic Agent continuously evaluates the performance of the user journeys and agent decisions:

• Assesses effectiveness, identifies gaps, and detects patterns in successful or unsuccessful journeys.

• Suggests improvements to other agents (e.g., “Switch channels,” “Adjust messaging tone”).

5. Planner Agent: Long-Term Journey Strategy

• A Planner Agent oversees strategic long-term goals for user journeys (e.g., defining a complete 14-day onboarding pathway).

• It ensures consistent user experiences aligned with broader business outcomes, guiding other agents with overarching strategic direction.

6. Reflective Learning & Continuous Improvement

• Questera employs a reflection-based learning pattern:

• Regularly analyzes past outcomes (“Why did the last five retention attempts fail?”).

• Adjusts tactics, channels, and messaging based on learnings.

• Continuously refines strategies, enabling agents to learn and improve over time.

Step	What Happens
1️⃣ User activity flows in	Real-time product + event data is ingested
2️⃣ Agent analyzes the journey stage	Uses behavioral patterns + funnel position
3️⃣ Next best action is chosen	Decides if/what message is needed now
4️⃣ Message is composed + sent	Fully contextual, dynamic, and timed
5️⃣ Outcome is tracked + learned	Success/failure loops back into future logic

1. Input Layer: Signals & State Awareness

• Ingests product, CRM, and behavior data

• Classifies users into journey stages (e.g., onboarding, drop-off, power user)

• Detects friction points, intent signals, and churn risks

2. Agent Collaboration Layer

Agent Type	Role in Orchestration
Activation Agent	Identifies drop-offs and deploys setup nudges
Churn Agent	Monitors disengagement, re-engages at-risk users
Expansion Agent	Spots upsell moments, delivers upgrade prompts
Reactivation Agent	Detects dormant users, personalizes winbacks
Routing Agent	Decides the best channel (email, in-app, SMS)
Coordination Agent	Resolves conflicts and prioritizes actions

Each agent owns a segment of the journey but can “talk” to others. For example:

Churn Agent detects risk → asks Routing Agent for best channel → Expansion Agent pauses upsell temporarily.

3. Execution Layer: Journey Orchestration

• Launches multi-step campaigns

• Adjusts content and channel mid-journey

• Dynamically updates based on user interaction

E.g., “User didn’t open email → resend via in-app”

4. Feedback Loop & Learning

• Agents observe what worked

• Update scoring models, message templates, and journey logic

• Next actions are smarter by default

This is what makes it a compounding system — not just a static one.