Pulsar-APS – Agentic Production Support for Batch Data Pipelines

Overview: Pulsar-APS (Agentic Production Support) is an intelligent production support technology artifact designed to transform how enterprises manage batch data pipeline operations. Leveraging a multi-agent AI architecture, Pulsar-APS continuously monitors batch schedules, detects failures within 30 seconds, autonomously diagnoses root causes, and resolves incidents across L1, L1.5, and L2 support tiers — with human-in-the-loop L3 escalation for complex cases. Pulsar-APS replaces reactive, human-centric support models with proactive, always-on intelligent agents that learn from every incident and compound institutional knowledge over time. Pulsar-APS is built as a core component of Coforge Data CosmosTM which is our Innovation Backbone combining platforms, agentic accelerators, and services to enable end-to-end data engineering, BI, governance, and analytics.

Core Capabilities:

1. Multi-Agent Autonomous Resolution Three specialized AI agents collaborate to resolve production issues: • Mr. Glass (L1 Pattern Matcher): Continuous 24/7 monitoring of ETL/ELT batch jobs, data mart refreshes, AI model training pipelines, and data quality check jobs. Automatically detects anomalies, creates ITIL-compliant tickets with P1–P4 severity classification, and resolves L1 issues via runbook execution including job resubmission, stale lock clearance, and credential refresh. • Mr. QuickFix (L1.5 Playbook Executor): Pattern-based resolution using a Known Error Database (KEDB). Applies learned remediation strategies including pipeline parameter adjustments, service restarts, connection pool resets, and retry with modified configurations. Makes up to 2 attempts before escalating. • Ms. Troubleshoot (L2 Deep Analyst): Deep diagnostic analysis for complex, multi-system failures. Performs database query optimization, data integrity verification, infrastructure-level interventions, cross-system dependency resolution, and root cause analysis.

2. Enterprise Orchestrator Integration Integrates natively with Apache Airflow (REST API/Webhooks), Control-M (Automation API), CA Autosys (CLI/REST API), and AWS Step Functions. Monitors up to 10,000 concurrent jobs across all connected orchestration engines with <30 second failure detection latency.

3. Integrated Ticket Management & KEDB Built-in JIRA-like ticket system with full lifecycle management (Open → In Progress → Resolved → Closed), P1–P4 severity levels, and configurable SLA management. Known Error Database auto-catalogs recurring issues, root causes, and proven resolutions, enabling agents to learn and improve with every incident.

4. Real-Time Dashboards & ITIL Reporting Command center dashboards with job run views, SLA compliance tracking, agent activity feeds, Kanban ticket boards, and system health monitoring. ITIL KPIs include MTTD, MTTR, FCR, SLA compliance percentage, and autonomous resolution rate. Reports include daily operations summary, weekly performance, monthly SLA, and executive summaries.

5. Trend Analysis & Predictive Intelligence Anomaly detection for job runtime patterns, predictive alerting for likely failures based on leading indicators, and seasonal pattern recognition (month-end spikes, quarterly cycles). Enables proactive issue prevention before SLA breaches occur.

6. Multi-Channel Communication & Knowledge Graph Agents communicate via email, Slack, and portal chatbot. Interactive knowledge graph visualization (powered by Neo4j) maps relationships between jobs, schedules, tables, systems, and data dependencies for impact analysis.

7. Cloud-Native Deployment on AWS Deployed as containerized microservices on Amazon EKS with Helm chart-based orchestration. Amazon Bedrock provides LLM reasoning for agent intelligence. Amazon S3 stores knowledge library artifacts. Amazon OpenSearch enables full-text search across KEDB and documentation. Supports auto-scaling policies based on workload metrics.

8. Model Resolution Scenarios • CRM Customer Sync (DB Connection Pool Exhausted): L1 auto-resolve. Resolution by Pulsar-APS in <3 min vs traditional Human led operations which takes 20–40 min. • Salesforce Pipeline (OAuth2 Token Expired): L1→L1.5. Resolution by Pulsar-APS in 4–6 min vs traditional Human led operations which takes 45+ min. • Finance GL Reconciliation (DECIMAL Schema Drift, novel): L1→L1.5→L2. Resolution by Pulsar-APS in ~8 vs traditional Human led operations which takes 45–90 min. • Enterprise Master Data (Disk Space Exhaustion): L1→L1.5→L2→L3. Human-led with complete diagnostic package.

Business Benefits: • 50–60% reduction in 5-year Total Cost of Ownership over traditional support models • 40–60% reduction in Mean Time to Resolution (MTTR) • 85%+ autonomous resolution rate across L1/L1.5 tiers • <30 second knowledge search latency (down from 15–25 minutes) • 99.5% SLA compliance target • 24/7 autonomous monitoring eliminating human fatigue errors • Complete audit trails with full telemetry and token cost tracking