Agentic AI in the Software Development Lifecycle: A Guide for Financial Services

Introduction

In the last few years, developing software applications for financial services has become increasingly difficult. With ever-changing regulations, business rules, interconnected transaction flows, and legacy platforms, the financial services industry faces constant pressure to balance security, governance, traceability, and, most importantly, customer service.

But it’s not all gloom.

In recent years, financial institutions have been adopting agentic AI in the software development lifecycle (SDLC).

Companies deploy specialized AI agents across requirements, design, development, quality engineering, deployment, and operations to build applications faster and meet regulatory and customer demands on time.

But how do they work in the SDLC, and how can financial services companies use them? We will answer all these questions here. We will also explore how multi-agent SDLC works, what companies should look for in an agentic SDLC platform, and why it matters to your company.

Let’s get started.

The Role of Agentic AI in the Software Development Lifecycle

What Is Agentic AI?

Agentic AI is an AI system that reasons, plans, and executes complex, multi-step workflows independently to achieve specific goals. AI agents are designed to understand, reason, generate, execute, analyze, and adapt based on the goal provided. They don’t require step-by-step prompts to execute the tasks. This saves time and reduces application development costs.

How is Traditional SDLC Different from Agentic SDLC?

Traditional SDLC was resource-intensive. Every step from requirement gathering to design, development, testing, implementation, deployment, and maintenance required human intervention.

While generative AI-assisted development exists, it still requires human intervention at every stage to drive actions.

With an agentic SDLC, humans don’t have to evaluate or provide commands at any stage. They define a goal for the AI agents, set criteria, and design a workflow the agents must follow. The agents then independently plan and execute the tasks, coordinate activities across relevant tools, analyze the results, and adapt their approach within the defined scope to achieve the goals. Human intervention is primarily limited to defining tasks, orchestrating agents, and reviewing their work.

To know more about Agentic AI and its role in SDLC, read the blog.

Why Financial Services Need Agentic AI for the SDLC?

1. Complex Rules, Legacy Platforms, and Stringent Regulatory Mandates

There is a lot of technical complexity and regulatory scrutiny involved in operating a financial institution. For example, banking and payments platforms must process interconnected transaction flows and adhere to scheme mandates, interchange rules, and business requirements within regulatory frameworks. Core legacy banking and payments platforms such as Fiserv VisionPLUS, HPS PowerCard, TSYS Prime, ACI CMM, Base24, Postilion, E6, Euronet ITM, Finnone, Finacle, and Intellect rely on legacy codebases and complex business logic. Even a specification change could involve analyzing large volumes of regulatory and business documentation updates. This makes the process time- and resource-intensive and also error-prone.

2. Automation maintenance

Test automation teams often spend 30-40% of their automation effort updating broken test scripts and locator references when the application UI or API changes. Agentic AI can improve script reusability, reduce regression cycle time, enable self-healing, and save the team effort.

3. Multi-day regression cycles

Regression suites for core banking and payment platforms can take several days, delaying releases. Agentic AI can test, design, automate, and optimize regression testing and accelerate the testing process.

4. Domain attrition

Critical business logic and domain knowledge remain undocumented and reside with experienced employees. So, if someone leaves, the system doesn't retain that knowledge. AI agents can use business rules, enterprise documents, domain knowledge bases, and banking ontologies to preserve this knowledge and gain better context for their work.

How Is Agentic AI Different from AI-Assisted Copilots?

While both AI-assisted copilots and agentic AI help users with specific tasks, they differ. In AI-assisted copilots, users have to decide what the AI model must do, evaluate the response, and deploy the next action. An agentic agent also needs human intervention. However, the user only needs to define a task or workflow within an approved scope. The agent plans, executes, analyzes, and adapts its actions independently, and the human user provides final approval at each sign-off gate. This makes agentic AI more resource-efficient and helps companies achieve goals faster without compromising compliance and business rules.

Use Cases for Agentic AI in Financial Services

1. Core Banking & Lending Systems

Agentic AI can help institutions analyze credit policies, manage the banking and lending systems, make credit and loan decisions, manage the collections workflows, and validate end-to-end loan journeys.

2. Payment Switching, Clearing & Settlement

The AI agents can help with payment switching transaction authorization, routing, ISO messaging, clearing settlements, and fee reconciliations. The agents can simulate multi-channel payment flows, validate clearing and settlement rules, and identify routing or reconciliation issues before production.

3. Cards Issuing & Acquiring

Card platforms support complex issuing and acquiring processes, including onboarding, tokenization, disputes, and scheme-mandate certifications. Agents can ingest rule changes, map them to test scenarios, generate compliant test data, and build or self-heal automation across card workflows.

4. Interchange Fee Structures & Merchant Clearing

Interchange calculations depend on variables such as merchant category, card type, entry mode, region, and volume. Specialized agents can extract business rules, build coverage across combinations, and validate calculations against merchant-clearing data.

5. ISO 20022 Compliance & Financial Message Flows

ISO 20022 adoption introduces new message structures and transformation requirements. Agents can parse schemas, generate valid message payloads, validate transformations between legacy and new formats, and trace message data through the payment chain.

6. Digital Banking & Open Banking APIs

Digital banking connects mobile and web channels with core systems through APIs and third-party integrations. Agents can test end-to-end customer flows, and self-heal automation when interface changes affect locators.

7. Automated Extraction of Financial Rules

Large regulatory rulebooks and business specifications require extensive manual analysis. Agents can extract underlying business rules, turn them into structured quality intelligence, and maintain traceability between requirements and test cases.

The Four Pillars Of The Agentic AI SDLC

Here’s how agentic AI is used across the entire SDLC:

1. Requirements & Design

In this stage, the specialized AI agents transform the business requirements into specifications, identify gaps, propose designs, and make informed architectural decisions.

2. Development & Build

In this stage, specialized AI agents generate and review code against the implementation plan and create unit tests.

3. Quality Engineering

In this stage, specialized testing agents generate test cases, create test data, execute tests, identify bugs and defects, fix failing scripts, and perform regression testing. This helps companies reduce repetitive work, save on testing time, improve test coverage, accuracy, defect detection, and maintenance.

4. Deployment & Operations

The specialized agents in this stage help with CI/CD orchestration, release activities, monitoring, incident triage, root-cause analysis, and remediation.

Why Every SDLC Pillar Needs Specialized Agents

Unlike general-purpose AI agents, specialized agents are designed to meet the unique objectives of each stage. Each stage uses different tools and artifacts and requires unique decision criteria, knowledge assets, permissions, and risk profiles. The specialized agents work together through an orchestrated workflow, share outputs, handle tasks, and seek human approval at required stages.

Here is a sneak preview of the specialized agents typically used at every stage of the SDLC:

Why Are Human-In-The-Loop And Domain Intelligence Necessary?

While agentic AI can handle the end-to-end SDLC process, it cannot eliminate the role of humans. Human experts are required to review and sign off on the test cases, code, and data. This ensures the application adheres to business, technical, regulatory, and risk requirements.

Another benefit of having a human-in-the-loop is their deep domain knowledge. An agent is only as good as the knowledge it has. Its decisions and outputs are dependent on human knowledge. For financial institutions, SME expertise, business rules, knowledge graphs, knowledge bases, BFS artifacts, institutional knowledge, and banking ontologies help preserve domain knowledge and ensure agentic workflows work accordingly.

A Checklist for Choosing an Agentic SDLC Platform

While choosing an agentic SDLC platform, ensure that it meets the following criteria:

1. Agent Orchestration

Ensure the platform’s orchestration engine can coordinate multiple agents across a defined workflow. It should support conditional branches, parallel execution, joins, and controlled handoffs so that complex SDLC tasks run as coordinated workflows rather than isolated AI interactions.

2. Human-in-the-Loop

The platform must allow critical outputs to be generated only after SMEs review and sign off. Agents must execute within the approved scope and shouldn’t independently sign off on artifacts without human approval.

3. Domain-Specific Knowledge

The platform should be able to train agents on enterprise artifacts, business rules, domain ontologies, knowledge bases, and other context relevant to the organization and the industry. This will help agents work with the business's terminology, rules, and workflows rather than relying only on generic model knowledge.

4. Security and Governance

Ensure the platform offers adequate controls around tenant isolation, agent identity, scoped permissions, credentials, audit trails, encryption, usage budgets, and compliance. The platform must also be transparent about what each agent does to support governance and maintain full audit trails.

5. Integration

Ensure the platform integrates seamlessly with the enterprise tools. Check if it can publish artifacts to systems such as JIRA, open GIT pull requests, and interact with CI/CD platforms without requiring manual hand-offs. This helps developers save time and effort by switching between systems and operating within the existing toolchain.

6. Deployment Flexibility

Check if the platform supports deployment models that fit enterprise security and data-residency requirements. For example, check if it can be deployed on a client VPC or on-premises, hybrid, and vendor-hosted environments. The vendor should clearly define where data is processed and stored and how sensitive test data is protected.

7. Ease of Adoption

Ensure the platform can deliver value through a short pilot without disrupting the existing development and deployment processes. It must adhere to the established scope, baseline KPIs, and success criteria to enable the institutions to commit to wider adoption.

8. No Lock-In

The platform should allow the institution to retain ownership of generated knowledge, test repositories, data packs, automation assets, and other artifacts. It must also offer the institution the flexibility to use them on other platforms or choose LLMs of their choice. This reduces reliance on a single vendor.

9. Output-Oriented Pricing

Ensure the pricing is linked to measurable delivery outcomes rather than the traditional time-and-materials headcount billing.

Introduction to Pragnos QE

What Is Pragnos QE?

Pragnos QE is an agentic quality engineering platform developed by Verinite Technologies Private Limited to solve the testing complexities of financial services applications. Unlike point solutions that address individual testing needs, Pragnos QE provides quality engineering solutions across four SDLC phases—requirements, design, development, and testing.

Pragnos leverages Verinite’s deep domain expertise in cards, payments, ISO 20022, and core lending that enables the AI agents to work efficiently within the context and deal with the complexities of financial services applications.

The platform offers specialized agents for Test Design, Test Automation, Self-Healing, Test Data, Defect Intelligence, and Regression Optimization. It helps teams automate and optimize quality engineering across the software lifecycle.

What Problems Does It Solve?

• Manual and repetitive quality-engineering work
• Slow test-design and execution cycles
• High automation-maintenance effort
• Long regression cycles
• Difficult test-data generation
• Limited reuse of automation scripts
• Defect leakage and ineffective defect detection
• Loss or fragmentation of specialized financial-services domain knowledge

Why Pragnos QE?

1. Pilot in Four Weeks, Steady State in Two Months

Pragnos QE uses a pod-based operating model comprising one BFS Domain SME/QE Lead, two QE Engineers, and 59 specialized AI agents across Test Design, Automation, Self-Heal, Test Data, Defect Intelligence, and Regression Optimizer. It covers the functional, channel, API, compatibility, and performance testing that is usually done by a 15-member testing team.

The implementation takes place in three stages:

• Weeks 0–2 are focused on discovery, access, and building the tenant Knowledge Layer from relevant specifications and testing artifacts
• Weeks 3–4 involve running a live pilot alongside existing processes to establish KPI baselines
• Month 2 onward, the platform focuses on scaling the agents across applications and reviewing performance against agreed KPIs

2. Committed KPIs

Pragnos QE uses an outcome-oriented KPI framework in which performance targets are defined during the initial four-week pilot and for three categories:

• Volume and productivity: Tracks test cases authored, test runs executed, automation scripts built, and synthetic data sets generated per person-day.
• Quality and correctness: Monitors test-case correctness, post-review rework, defect-detection effectiveness, and automation false-positive rates.
• Reusability and effort saved: Checks for script component reuse, impact-based regression optimization, self-heal success, and person-days saved against the baseline.

3. No T&M Model

Pargnos QE follows a non-T&M model. Pricing is based on a predictable base fee with a KPI-linked variable component, or fixed pricing based on defined outcomes such as a regression cycle, application migration, or test-suite conversion.

4. Human-in-the-Loop

Human SMEs remain part of the Pragnos QE’s operating model. Agents propose and execute within their defined scope, while SMEs review and approve outputs at mandatory gates. This ensures human accountability while automating repetitive execution.

5. Deep Domain Knowledge as a Moat

Developed by Verinite, Pargnos QE’s knowledge layer is rooted in banking, payments, cards, lending, ISO 20022, digital banking, and financial-services workflows.

6. Deployment Flexibility

Pragnos QE supports Verinite-hosted, client-hosted, and hybrid deployment models. Client-hosted and hybrid configurations can keep data within the client's perimeter.

7. Security and Governance

Pragnos QE provides security and governance controls such as identity management, scoped permissions, role-based access, audit trails, encryption in transit and at rest, PCI DSS enforcement, compliance mapping, tenant isolation, and per-tenant model keys and budget controls. More importantly, the test data is synthetic or masked, so the client data is not used to test or train the models.

FAQs for Pragnos QE

Q. How secure is the customer data?

Pragnos supports Verinite-hosted, client-hosted, and hybrid deployment models. Client-hosted and hybrid configurations can keep data within the client's perimeter. The material also describes synthetic or masked test data and states that client data is not used for model training.

Q. How do we know an agent has not acted outside its scope?

The platform uses identity, scoped permissions, agent governance, and a full action audit trail. Its architecture also includes defined agent responsibilities and human approval gates. This ensures that the agents perform the tasks within the defined scope.

Q. Can we trust agent output without human checking?

The operating model is explicitly human-in-the-loop. Agents do not sign off; SMEs review and approve designated artifacts.

Q. What is the pricing model of Pragnos QE?

The Pragnos QE model is positioned around measurable outcomes and committed KPIs rather than traditional time-and-materials billing. The KPI framework includes productivity, quality, regression, self-healing, and effort-saved measures.

Q. Are users locked into the platform?

The platform offers exit-ready portability, and supports model-agnostic routing and bring-your-own-LLM capabilities. So, users are not tied to a particular platform.

For more details about Pragnos QE, contact us.