Case Study

Building an Enterprise-Grade, Governed Agentic AI Operating Model Across the IT Landscape

Industry

Banking and Financial Services

Location

Global

Our Contributions

Agentic AI Strategy and Engineering

A leading global bank partnered with Coforge to architect and implement an enterprise-grade agentic AI operating model across its IT landscape. We built a governed, scalable pipeline for agent creation, certification, change management, and autonomous operations, transforming IT from manual and reactive to AI-governed and self-optimizing.

Transformation Timeline

Phase 1

Agent Playbook

Standardized blueprint and factory for reusable, compliant agent creation.

Phase 2

Agent Validation and Certification

Six-gate validation pipeline ensuring every agent is production-ready.

Phase 3

Change Management

Two-tier OCM strategy bridging the human–AI trust gap across IT teams.

Phase 4

AI Operations

Real-time telemetry, cognitive monitoring, and AI-governed autonomous IT.

Drag

The Challenge

The client was having difficulty moving from isolated, experimental AI deployments to an enterprise-wide agentic operating model. Each new AI agent required custom development, security review, and integration, driving up costs and time-to-market.

Operationally, IT was reactive and dependent on manual triage, ticket queues, and human escalation. Their goal was AI-governed IT: letting autonomous agents handle incident resolution while leadership focuses on oversight, management, and continuous improvement.

They needed a governed lifecycle to ensure every agent met stringent compliance and security standards, as well as careful change management to overcome the workforce’s lack of trust in autonomous agents.

Our Approach

Agent Creation Playbook

Designed a reusable, plug-and-play agent factory built on a hybrid LangGraph and Microsoft Azure ecosystem. The playbook bakes zero-trust security and compliance directly into the foundation, so every subsequent agent inherits enterprise-grade protections by default, collapsing development timelines and lowering marginal cost at scale.

Agent Validation as a Service (AVaaS)

Implemented a rigorous certification pipeline that serves as the non-negotiable stage-gate before any agent enters the client’s production environment, covering capability validation, context integrity, responsible AI, red teaming, workflow safety, and continuous observability.

Organization Change Management (OCM)

Deployed a two-tier OCM strategy that treats agent introduction and feature rollout as distinct change events. Using ADKAR principles embedded in every sprint and a wave-based rollout model, the program builds measurable workforce confidence and adoption at each stage, treating resistance as data rather than friction.

AI Operations & Cognitive Monitoring

Established real-time telemetry infrastructure to provide continuous oversight of the agent fleet, including cognitive drift tracking. A supervisory model automatically detects and initiates recalibration when agent performance deviates, completing a closed-loop feedback cycle that refines the next generation of agents.

Impact to Date

45 active agents

In production fleet, tracked for health and cost

87% auto-resolution rate

Incidents resolved without human intervention

7 SDLC agents

Initial playbook deployment across the software lifecycle