Agent Architecture

Technical overview of Moklo's AI agent system and decision-making framework

Overview

Moklo's agent architecture is built on a modular, event-driven framework that enables autonomous decision-making across the outbound sales lifecycle. Unlike traditional rule-based automation, our agents employ machine learning models, natural language processing, and real-time data analysis to adapt strategies dynamically.

Core Agent Types

Research Agent

The Research Agent is responsible for prospect intelligence gathering and enrichment. It continuously monitors multiple data sources to build comprehensive prospect profiles.

• Data Sources: Company websites, social media, news feeds, job postings, technology databases, funding announcements
• Enrichment: Firmographic data, technographic signals, intent indicators, organizational hierarchy
• Output: Structured prospect profiles with confidence scores for each data point

Strategist Agent

The Strategist Agent analyzes prospect profiles and campaign objectives to determine optimal outreach strategies.

• Channel Selection: Determines whether to lead with SMS, email, RVM, or voice based on prospect characteristics
• Timing Optimization: Predicts optimal send times using historical engagement data and industry patterns
• Sequence Design: Creates multi-touch campaigns with adaptive follow-up logic
• Personalization Strategy: Identifies key talking points and value propositions for each prospect

Copywriting Agent

The Copywriting Agent generates personalized messages using large language models fine-tuned on high-performing sales copy.

• Message Generation: Creates unique copy for each prospect incorporating research insights
• Tone Adaptation: Adjusts formality, urgency, and style based on industry and role
• A/B Testing: Generates message variants for continuous optimization
• Compliance Checking: Ensures messages meet regulatory requirements and avoid spam triggers

Engagement Agent

The Engagement Agent monitors prospect responses and determines appropriate follow-up actions.

• Response Analysis: Uses NLP to classify responses (interested, not interested, out of office, etc.)
• Intent Detection: Identifies buying signals and qualification indicators
• Conversation Management: Handles common objections and questions autonomously
• Escalation Logic: Routes qualified prospects to human sales reps with full context

Optimization Agent

The Optimization Agent continuously analyzes campaign performance and adjusts strategies to improve results.

• Performance Monitoring: Tracks response rates, conversion metrics, and engagement patterns
• Pattern Recognition: Identifies successful strategies and replicates them across campaigns
• Anomaly Detection: Flags deliverability issues, compliance risks, or performance degradation
• Recommendation Engine: Suggests campaign adjustments to improve outcomes

Decision-Making Framework

Moklo agents make decisions through a hierarchical evaluation process:

Data Collection: Agents gather relevant information from internal databases and external sources
Context Analysis: Machine learning models analyze the data to understand prospect context and campaign state
Strategy Generation: Agents generate multiple potential actions with predicted outcomes
Evaluation: Each action is scored based on expected impact, compliance risk, and resource cost
Execution: The highest-scoring action is executed with appropriate monitoring
Learning: Actual outcomes are compared to predictions to refine future decision-making

Agent Communication Protocol

Agents communicate through an event-driven messaging system that enables coordination without tight coupling:

Event: prospect_enriched
  ↓
Research Agent → Strategist Agent
  ↓
Event: strategy_created
  ↓
Strategist Agent → Copywriting Agent
  ↓
Event: message_drafted
  ↓
Copywriting Agent → Execution Engine
  ↓
Event: message_sent
  ↓
Execution Engine → Engagement Agent
  ↓
Event: response_received
  ↓
Engagement Agent → [Strategist Agent | Human Rep]

Machine Learning Models

Moklo employs several specialized ML models within the agent framework:

Channel Prediction Model

Predicts which communication channel will generate highest engagement for each prospect

Timing Optimization Model

Determines optimal send times based on industry, role, and historical patterns

Response Classification Model

Categorizes prospect responses to determine appropriate follow-up actions

Lead Scoring Model

Predicts conversion likelihood based on engagement signals and prospect characteristics

Scalability & Performance

The agent architecture is designed for horizontal scalability. Each agent type runs as an independent service that can be scaled based on workload. The system processes thousands of decisions per second while maintaining sub-100ms response times for critical operations. Agent state is persisted in distributed databases to ensure reliability and enable audit trails.

Learn More

Explore how our agents work together in the sequencing engine and compliance framework.

Sequencing Engine →Compliance →