Avekshaa Technologies Introduces Performance Engineering Framework for AI-Agent and Agentic Payment Systems

Avekshaa Technologies, a specialist in Application Performance Engineering, Management, and Quality Assurance for the BFSI sector, today announced a dedicated performance engineering framework built specifically for AI-agent and agentic payment systems. The framework extends Avekshaa's proprietary P-A-S-S Assurance Platform to address a new category of risk emerging as banks and fintechs move from rule-based automation to autonomous, decision-making AI agents.

Agentic AI is moving quickly from pilot projects into production across banking, payments, and financial services. Unlike traditional automation, these systems plan multistep actions, call external tools and APIs, and make decisions with limited human oversight. In payments specifically, agents are increasingly involved in tasks such as transaction routing, fraud triage, reconciliation, and customer-initiated fund transfers, often chaining several model calls and third-party services together in a single workflow.

This shift introduces performance and reliability challenges that conventional load and functional testing were never designed to catch. An AI agent's response time can vary based on the complexity of its reasoning path. A single transaction may trigger multiple model inferences, tool calls, and API round trips, each adding latency and each representing a potential point of failure. Under peak concurrency, these compounding delays can breach service level agreements, disrupt payment cutoffs, or produce inconsistent decisions across otherwise identical transactions.

"Agentic systems don't fail the way traditional applications fail. The bottleneck might not be the database or the network. It could be an unpredictable reasoning loop, a slow upstream model call, or a tool invocation that behaves differently under load than it did in a controlled test," said Rajinder Gandotra, Founder and CEO of Avekshaa Technologies. "Banks and payment providers need a way to validate these systems for performance, scalability, and decision consistency before they go live, not after a customer-facing incident forces the question."

Avekshaa's new framework addresses this gap through a combination of workload modelling, agent-chain latency profiling, and resilience validation. Key components include:

Agent-chain latency mapping: Tracing the full path of an agentic transaction, including model inference calls, tool and API invocations, and orchestration layers, to identify where delays accumulate under concurrent load.
Concurrency and scale testing for autonomous workflows: Simulating realistic transaction volumes and peak-hour surges to validate how agentic payment flows behave when thousands of sessions run simultaneously.
Decision consistency checks under load: Verifying that an agent's output for a given transaction type remains stable and compliant even as system load increases, reducing the risk of inconsistent or erroneous decisions during peak periods.
Failure and fallback validation: Testing how agentic systems degrade when a dependent model, tool, or third-party service is slow or unavailable, and confirming that fallback paths preserve transaction integrity.
Integration with existing core and middleware layers: Assessing how agentic components interact with core banking systems, payment rails, and middleware to surface bottlenecks that only appear when these layers operate together.

The framework builds on Avekshaa's two-decade track record in performance engineering for mission-critical BFSI systems, including end-to-end customer journeys across mobile and web, API and microservices layers, core and middleware hops, and payments rails, delivered for some of India's largest banks. The company is applying this same shift-left, evidence-based approach to a category of technology that is advancing faster than most institutions' internal testing capabilities.

Industry analysts have noted that the transition to agentic AI in banking carries real operational stakes alongside its efficiency gains. A recent Deloitte analysis of agentic AI adoption among US banks observed that these systems may take time to yield robust gains, and that the benefits of speed and efficiency need to be weighed against risk considerations such as data privacy, ethical dilemmas, and regulatory compliance. Avekshaa's framework is designed to give institutions objective, evidence-backed answers to these risk questions before an agentic system is exposed to real customers and real money.

Avekshaa is currently working with select banking and fintech partners to pilot the framework ahead of a broader rollout. Organizations planning to deploy AI agents in customer-facing or transaction-processing roles can learn more about Avekshaa's approach to performance testing for banking applications and its wider application performance engineering services for banks.
About Avekshaa Technologies
Avekshaa Technologies is a leading specialist in Application Performance Engineering, Management, and Quality Assurance, helping enterprises across BFSI, telecom, and retail safeguard revenue and brand reputation through proactive, evidence-based performance assurance. Powered by its proprietary P-A-S-S Assurance Platform, Avekshaa has worked with more than 60 enterprise customers, including some of India's largest banks, to prevent and resolve performance, availability, and scalability risks before they reach production. The company is headquartered in Bangalore, India, with a growing global presence.