Govern Agent-Human (Hybrid) Customer Journeys

Hybrid customer journey governance is the capability to identify, attribute, and govern interactions where a human customer and an AI agent (or automation acting on their behalf) both participate in the same journey across discovery, evaluation, support, onboarding, and transactions.

In hybrid journeys:

• A human may browse directly while an agent completes checkout
• An agent may research on behalf of a customer, then the human finalizes decisions
• Both human and agent sessions may occur in parallel or sequentially

CHEQ provides a single trust layer that classifies entities, links agents to underlying customers, and supports policy-driven controls across all journey stages.

CHEQ evaluates three correlated intelligence streams, each contributing a different dimension to entity classification:

• Traffic Intelligence: Automation indicators (headless browsers, automation frameworks), device/browser spoofing, network anomalies, and behavioral deviation from human norms
• Trust Intelligence: Script and tag execution behavior, consent enforcement, data leakage signals, and unapproved vendor activity
• Identity Intelligence: Identity resolution across sessions, synthetic identity indicators, identity risk scoring, and cross-session consistency

By correlating these signals, CHEQ classifies humans, known agents, unknown agents, good bots, and malicious automation with explainable evidence supporting each determination.

Human-agent linkage connects agent activity to the underlying human customer. CHEQ applies two complementary models depending on session state:

Pre-auth linkage (probabilistic): In unauthenticated sessions, CHEQ associates agent activity to a likely human using IP/network consistency, device continuity, familiar usage patterns, and identity network signals. This supports measurement, segmentation, and risk policy before login.

Post-auth linkage (deterministic): In authenticated sessions, CHEQ directly connects agent actions to the logged-in user with high confidence, supporting strict enforcement for sensitive actions and transactions.

Linkage enables reporting on which agents customers use, conversion differences in hybrid vs human-only journeys, and risk attribution.

CHEQ supports policy-driven, proportional enforcement rather than binary allow/block decisions:

• Allow: Known, trusted agents and legitimate automation proceed without friction
• Allow with limits: Rate, endpoint, or action constraints for moderate-trust scenarios
• Step-up: Additional verification (MFA, challenge) for elevated risk
• Suppress scripts/tools: Reduce data exposure and tool activation for low-trust sessions
• Throttle/constrain: Limit request rates or specific actions
• Block: Stop high-risk, adversarial automation

Policies are calibrated to customer-defined business logic and tuned using performance outcomes and false-positive/false-negative tradeoffs.

CHEQ supports continuous trust evaluation at critical transitions:

• Visit / Engage: Discovery and browsing classification
• Sign-up / Register: Account creation and identity validation
• Login / Account Access: Authentication and session establishment
• PII Access: Sensitive data viewing or modification
• Checkout / Purchase: Transaction and payment flow
• High-risk events: Password reset, address change, payment method update, entitlement changes

Trust decisions at each stage use current entity classification, linkage confidence, and risk signals to apply appropriate controls.

Yes. CHEQ helps identify spoofed agents and impersonation attempts by evaluating:

• Declared vs undeclared agents: Comparing self-declared identity to correlated environmental and behavioral signals
• Device and browser spoofing: Detecting inconsistencies in declared vs actual device/browser properties
• Network anomalies: Masked origins, VPN/proxy patterns inconsistent with customer profiles
• Identity inconsistencies: Synthetic identity indicators, AI-generated identity elements, and cross-session anomalies

This helps reduce risk from adversarial automation attempting to impersonate legitimate agents or customers.

CHEQ provides reporting and analytics that show:

• Which agents customers are using: Specific AI assistants and automation platforms detected in journeys
• Agent participation rates by journey stage: Percentage of sessions with agent activity at discovery, evaluation, checkout, and support
• Conversion and drop-off differences: How hybrid journeys compare to human-only journeys in funnel performance
• Linkage confidence: Pre-auth probabilistic vs post-auth deterministic attribution quality

These insights support measurement, segmentation, and business logic calibration.

CHEQ supports enabling legitimate agent-assisted experiences, including:

• Consumer AI shopping assistants acting on customer behalf
• Procurement and reorder automation in B2B contexts
• Accessibility tools and assistive technologies
• Known API integrations and partner workflows
• Customer-authorized automation for routine tasks

Policies distinguish these from adversarial automation like scraping, scalping, fraud, data extraction, and account abuse based on authenticity, intent, and observed behavior.

CHEQ delivers entity labels, linkage indicators, risk scores, and verdicts via:

• Real-time APIs: Online decisioning for CDN, WAF, IAM, and application enforcement
• Event streams / data layer injection: Real-time signals to analytics, CDP, and activation platforms
• Batch exports: CSV/JSON via storage/log pipelines for offline analysis
• Native integrations: Direct connections to analytics, CDP/CRM, SIEM/SOAR, and edge/CDN enforcement points

This “data anywhere” approach supports enforcement, measurement, segmentation, and investigation across your stack.

CHEQ uses Business Logic Calibration & Policy Management:

1. Receive Truth Data: Customer provides ground truth about legitimate vs adversarial activity
2. Intelligence Calibration: CHEQ tunes detection models to customer reality and business context
3. Confirm & Make Policy with Customer: Policies are configured based on customer-defined acceptable friction and risk tolerance

Calibration is ongoing as threats, traffic patterns, and business rules evolve. Policies can be adjusted through configuration rather than application rewrites, depending on integration pattern.