AI-assisted engineering

It’s a typical workday on the project floor. A release checkpoint is due, and the scrum master is juggling multiple tasks—clarifying user stories, checking test results, and ensuring the latest build is on track.

Instead of pinging different teams, they simply type: “What’s our project’s current release readiness?”

Seconds later, an AI agent responds with a single view, pulling data from the continuous integration / continuous deployment (CI/CD) pipeline, scanning build logs, flagging failures, and surfacing unresolved Jira tickets.

At the same time, other agents assess story clarity and auto-generate regression tests—quietly coordinating across roles in the software delivery lifecycle. The workday looks the same. The workflow hasn’t changed. What’s changed is how the work gets done.

The evolution: From isolated tasks to agent-led collaboration

For years, engineering automation targeted individual tasks like code completion or test generation, boosting productivity but leaving the broader software delivery lifecycle reliant on manual effort. Virtusa is augmenting tools like Copilot, identifying where effort persisted across planning, development, testing, and deployment, and then introducing modular agents to orchestrate work across roles and systems.

It mirrored the evolution of genAI from prompt-driven tools to agentic AI or problem-solving agents. Instead of prompting a large language module (LLM) five times to complete five tasks, teams can now assign a single goal—validate requirements, assess build readiness, or triage issues—and let the agent handle the sequence. It breaks down tasks, decides when to invoke an LLM, fetch data, or trigger workflows, and executes end-to-end. A business analyst can get the Clarity score and suggestions to improve the requirements. A project lead can request a build status and receive a full report with traceable logs. The interaction is no longer just machine-to-machine. It’s a machine that understands human goals and acts on them on its own.

Orchestration by design, not dependency

As the number of agents grows across the software lifecycle, coordination becomes critical. At Virtusa, the response is not just to build more agents, but to build them for interoperability.

Each agent is designed using industry-standard communication protocols, including agent-to-agent (A2A) and modular orchestration messaging. This ensures agents can operate as a cluster, either independently or in tandem, across different stages of the Software Development Lifecycle (SDLC). Whether it’s a requirement-scoring agent supporting business analysts or a regression-testing agent aiding QA, each can be invoked through natural language, deployed in local environments, or run centrally from a shared platform.

The orchestration platform envisioned by Virtusa acts as an agent registry—a structured layer where agents across domains (engineering, testing, data, domain-specific use cases) can be hosted, discovered, and executed. Its design is intentionally flexible. Clients may consume a single agent, integrate it into an existing portal, or deploy a full stack across teams.

This enables engineering without lock-in. The same agents that power Virtusa’s accelerators—like Virtusa Helio Canvas, Lumos, or App Analyzer—can now be accessed through orchestration or integrated directly into a client’s IDE, CI/CD toolchain, or BA dashboard. 

Supporting this model is a library of reusable components—prompt templates, metadata schemas, scoring patterns, and configuration blueprints—that allow teams to create or customize agents without starting from scratch. These assets can be reused across projects and pipelines, reducing manual effort and enabling faster, more consistent delivery. The orchestration is modular. The intelligence travels with the agent.    

Outcomes: what changes, and for whom

The benefits of AI-assisted engineering show up in the daily rhythms of software teams. It’s not just faster output—it’s about removing friction between roles, systems, and decisions. A scrum lead gets real-time release readiness from a single agent query. A business analyst sees a requirement clarity score before development begins. A tester receives auto-generated regression suites tied to code changes, cutting hours from the QA cycle.

In Greenfield development, agentic automation has increased team productivity by 15–30%, with higher returns—up to 60–80%—in focused tasks such as test generation, API coding, or DevOps orchestration. Even in legacy modernization, tools like App Analyzer accelerate documentation recovery and technical assessment.

But the broader outcome is team velocity. When intelligence is embedded into the workflow—through tools that learn, agents that act, and interfaces that understand—delivery becomes more transparent, predictable, and collaborative.

Redefining how engineering gets done

The software lifecycle hasn’t changed. The expectations around it have.

Engineering teams are still accountable for speed, quality, and scale. Now, they have tools that change how these goals are met. 

Computing has always been about instructing machines to get work done. What’s different now is the language we use, the systems we build, and the degree of autonomy we allow. AI agents now operate not just alongside engineers, but sometimes independently, taking context-aware actions without human prompts. To be sure, agentic AI isn’t about replacing engineers. It’s about removing barriers, scaling expertise, and focusing human effort where it matters most. As AI evolves, engineering isn’t being displaced. It’s being redefined—intelligently, incrementally, and at the pace of modern delivery.