AI Agents Automate Multi-System Workflows | Case Study

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Client Overview

The client is a prominent marketing automation firm managing thousands of campaigns each month across industries including real estate, healthcare, and education. The organization relies on multiple operational systems to manage contact records, reporting schedules, and lead notifications.

As campaign volumes increased, contact update requests grew rapidly, creating operational bottlenecks across departments and systems.

Problem Statement

Contact updates were initiated via email and manually executed across multiple platforms, including CRM, reporting tools, and communication systems.

Each request required navigating 10 to 15 steps across systems. This process was time-consuming, prone to human error, and difficult to scale. Inconsistent contact data across tools led to reporting discrepancies, missed notifications, and SLA risks.

Key Challenges

  • Fragmented system landscape

    Contact data was spread across CRM, reporting, and communication platforms.

  • Manual multi-step workflows

    Each update required repeated data entry across five systems.

  • Data inconsistency risks

    Human errors caused mismatched contact details and reporting failures.

  • Limited scalability

    50 to 100 weekly update requests strained operational teams.

Solution Implemented

Credencys designed and deployed an autonomous AI agent framework to orchestrate contact updates end to end with zero-touch automation.

Key solution components included:

  • Email trigger agent: Monitored a dedicated inbox and automatically initiated workflows upon receiving update requests.

  • Intent classification agent: Used LLM-based logic to classify requests as add, modify, or remove and extract relevant contact attributes.

  • CRM orchestration agent: Created or updated contact records, generated structured tasks, and applied business rules within the CRM system.

  • Reporting system agent: Updated recipient lists across active campaign and reporting schedules.

  • Notification system agent: Modified lead summaries, alerts, and communication settings across platforms.

  • Confirmation and audit agent: Sent automated confirmations to requestors and maintained execution logs for full traceability.

  • Human-in-the-loop exception handling: Escalated ambiguous or complex requests to designated team members.

Business Impact

The AI agent deployment significantly improved operational efficiency and data consistency:

  • Over 85% of contact workflows fully automated,

    reducing dependency on manual processing



  • End-to-end update time reduced from 25 minutes to under 2 minutes,

    dramatically improving responsiveness

  • 99.5% data consistency rate across systems,

    eliminating cross-platform discrepancies

  • 5x increase in weekly update handling capacity,

    without expanding operational headcount

Highlights

  • 85% automation of contact workflows
  • Under 2-minute average resolution time
  • 99.5% contact data consistency rate

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