How Does Questera Work
Questera’s Agentic Engine: Under the Hood
Questera’s Agentic Engine orchestrates sophisticated, personalized customer interactions at scale by combining cutting-edge AI technologies into a fully automated, self-improving system.
The system operates through five deeply integrated modules:
1. Perception - Real-Time Observation Layer
Continuously monitors and ingests user interactions, contextual data, and engagement events from multiple sources (e.g., in-app behaviors, email clicks, transactions, session duration).
How it Works:
Real-time Data Ingestion: Captures behavioral events (clicks, views, purchases, abandonment) directly from sources such as Segment, Snowflake, CDPs, or streaming APIs. Remembers past states and transitions, allowing agents to recognize patterns and respond with increasingly relevant actions.
Contextual Memory & Signal Processing: Maintains dynamic user profiles with session states, recent activities, historical patterns, and significant behavioral events. Continuously stores and maintains historical user interactions and behaviors, such as clicks, conversions, and engagement.
Embedding Generation: Converts complex user interactions into numerical representations (embeddings) that can be effectively used for downstream analysis. Maintains an updated timeline of user actions in vectorized embeddings, making past interactions easily retrievable and actionable.
Capturing Real-time Context: Embedding tables store dense vector representations of real-time user interactions—such as clicks, page views, product engagements, email opens, and transactional events.
Session & Behavior Modeling: Events are embedded into vectors representing contextual similarity, making it easier for downstream models to reason about user behavior at scale.
Event Sequences & Historical Context: Historical interaction patterns (past purchases, churn signals) are maintained within embedding tables to rapidly retrieve past behaviors similar to current scenarios.
Technical Stack:
Data infrastructure (Snowflake, Segment, real-time event streams)
Embedding generation (transformer-based encoders, embeddings API)
Event-driven architectures (Kafka, event streams)
2. Intent Detection - Predictive User Understanding
Analyzes and interprets real-time and historical user behavior to accurately predict a user’s current intent, goal, or lifecycle stage (activation, engagement, risk of churn).
How it Works:
Embedding Analysis: Uses neural networks trained on historical user journeys to classify user intent (e.g., purchase intent, churn risk).
Real-time Goal Classification: Classifies each user interaction into specific goals, like onboarding completion, upsell readiness, or reactivation need.
Dynamic State Machine: Updates a user's state dynamically, adjusting as the user progresses through different funnel stages or behavioral states.
Technical Stack:
Neural network classifiers (PyTorch, TensorFlow)
Next-best-action (NBA) predictive modeling
Real-time streaming ML inference (low-latency prediction)
3. Planning - Strategy Decision Engine
Determines the optimal personalized strategy for user engagement by choosing the next best actions (messages, content, offers, timing) to achieve specific goals.
How it Works:
Autonomous decision-making agents leverage contextual memory and real-time data to optimize their choices of next-best-actions (message content, offer selection, timing).
Personalizes messages by employing retrieval-augmented generation (RAG) and generative AI to utilize both historical context (user preferences, previous interactions) and real-time context (current behaviors, active signals).
Embeddings provide contextual inputs (user profiles, historical behaviors, intent embeddings) for reinforcement learning and contextual bandit models. These embeddings allow agents to rapidly decide on optimal strategies based on context similarity and historical success.
Reinforcement Learning (RL): Trains RL models on historical data to learn optimal decision policies by predicting and maximizing long-term goals (e.g., Lifetime Value, retention rates, activation success).
Contextual Multi-Armed Bandits: Continuously evaluates user responses in real-time, dynamically selecting the most effective actions based on immediate context.
Policy Engine: Manages decision logic through a combination of business rules, learned policies, and predictive models.
Technical Stack:
Reinforcement learning models (Contextual Bandits, Deep Q-Networks)
Policy management framework (custom orchestration and rules engines)
Experimentation and optimization libraries (OpenAI Gym, custom RL agents).
4. Action - Real-Time Orchestration Layer
Executes the optimal actions decided by the planning layer across multiple marketing channels—such as email, SMS, push notifications, or in-app messages—in a seamless, automated manner.
How it Works:
Unified Campaign Deployment: Automatically generates personalized messaging variations using Retrieval-Augmented Generation (RAG) and fine-tuned GPT models.
Channel Selection & Personalization: Dynamically selects optimal messaging channels based on user preference, historical engagement, and real-time responses.
Automated Execution: Integrates with existing marketing platforms (e.g., Braze, Twilio, SendGrid) to deploy messages instantly without manual intervention.
Adaptive Response: Quickly adjusts campaigns based on immediate user feedback—escalating actions when users engage, backing off when users become unresponsive.
Technical Stack:
Retrieval-Augmented Generation (RAG) for personalized content
GPT-based language generation (OpenAI, Anthropic)
APIs and integrations with marketing platforms (Braze, Iterable, Twilio, Webhooks)
5. Feedback & Continuous Learning (Self-Optimizing Loop)
Continuously evaluates performance and incorporates real-time results into the agent’s learning loop to systematically improve future decisions.
How it Works:
Outcome Tracking: Captures detailed performance metrics (open rates, clicks, conversions, churn, revenue impact).
Reward Functions: Assigns numeric weights to successful and unsuccessful outcomes (e.g., positive conversions rewarded, negative experiences penalized).
Continuous Retraining: Regularly re-trains the RL policy and intent models using the latest engagement data (offline reinforcement learning, incremental training).
Adaptive Memory: Updates contextual embeddings and agent memories based on real-time user interactions, ensuring continuously improving performance.
Technical Stack:
Offline RL training pipelines
Incremental model retraining (Airflow, Kubeflow, MLFlow)
Model and embedding storage (Vector DBs, Pinecone, Redis for real-time embeddings)
How Questera's Multi-Agentic Engine Works
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.
How Multi-Agent Orchestration Fits Across Layers:
Cross-Layer Coordination: Questera’s agentic orchestration spans horizontally across all four layers. Agents communicate through shared memory/context embeddings and decision logic:
Observation agents at perception layer share embeddings and context with intent layer agents.
Intent detection agents inform decision-making agents of inferred goals and signals.
Planning and scheduler agents coordinate actions to ensure coherent and prioritized experiences.
Execution agents deploy strategies and feed outcomes back up to adjust future agentic decisions.
Inter-Agent Collaboration & Competition: Each agent focuses on specific outcomes (onboarding, retention, upsell), collaboratively working from a unified user profile and context memory. They simultaneously negotiate and resolve conflicts using the scheduler and critic agents—ensuring cohesive, prioritized user engagement.
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