Tata Consultancy Services (TCS) runs large-scale testing for Banking, Financial Services, and Insurance (BFSI) clients, where the slowest part of a release is rarely writing application code. The larger constraint is the chain of work that follows: interpreting requirements, designing scenarios, writing and maintaining automation, preparing execution environments, investigating failures, and assembling evidence for review.
What happened
The larger constraint is the chain of work that follows: interpreting requirements, designing scenarios, writing and maintaining automation, preparing execution environments, investigating failures, and assembling evidence for review.
Where the sources line up
BFSI clients need to increase testing throughput while preserving the controls expected in financial services. The objective wasn’t to replace quality engineers. It was to remove repetitive translation and execution work so that engineers could spend more time on risk analysis, exploratory testing, and release decisions. These industry needs generated four specific requirements for TCS. First, every test must be grounded in project-specific context rather than generic prompts. Second, it had to cover both API and browser-based UI testing to match how BFSI applications are built. Third, security is paramount, so every tenant’s data and knowledge store must be isolated.
Practical impact for readers
TCS already uses traditional test automation for its clients, and it does improve execution speed. But it leaves the slow work untouched: teams must still create the scenarios, convert them into scripts, maintain locators and dependencies, and diagnose failures. When a linked user story changes, scripts generated for an earlier story can quickly become stale. A single generative AI prompt also doesn’t provide the control needed for a multi-stage engineering workflow.
Who should pay attention now
TCS therefore designed the Agentic Test Orchestrator to decompose testing into specialized tasks. Each task has its own agent, tools, validation rules, and context. A deterministic supervisor routes work, while an execution workflow controls retries, error classification, persistence, and reporting.
What is still unclear
To run these agents, TCS chose Amazon Bedrock AgentCore , a service to build, connect, and optimize agents at scale, with any framework or model. Three capabilities drove the decision. First, Amazon Bedrock offers a choice of models, so each agent uses the model that fits its task, such as scenario design or code generation, and TCS can adopt newer models as they become available. Second, AgentCore runtime, a capability of Amazon Bedrock AgentCore, scales with demand, so testers across many tenants can run generation and execution sessions at the same time without TCS provisioning or tuning agent servers.
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