Agentic Software Engineering – AI Driven Platform Engineering

From AI Coding to Agentic Engineering

Most AI coding tools operate within isolated, task-level contexts, making it difficult to maintain consistency, governance, and visibility across enterprise software delivery. Agentic Software Engineering (ASE) addresses this through an Agentic Factory that provides shared context, centralized orchestration, and enterprise oversight across the SDLC.

More

Most AI coding tools operate within isolated, task-level contexts, making it difficult to maintain consistency, governance, and visibility across enterprise software delivery. Agentic Software Engineering (ASE) addresses this through an Agentic Factory that provides shared context, centralized orchestration, and enterprise oversight across the SDLC.

Work progresses through three governed stages: Refinement, where AI and human experts transform requests into approved specifications; Execution, where specialized agents build and validate work against those specifications; and Verification, where outputs are tested against original requirements before release.

By combining AI agents, engineering practices, persistent context, and human control within a unified delivery model, ASE improves quality, traceability, compliance, and delivery velocity while enabling organizations to scale AI-powered engineering with confidence.

Less

How ASE Works

ASE rests on four foundational components that enable governed, measurable, and repeatable software delivery at enterprise scale.

Agentic Vector Squad Model

The agentic-era delivery unit that combines Agentic Application Engineers with pre-built AI agent teams. Human engineers define scope, review specifications, and assure quality, while agents execute work across the lifecycle. Delivered as a single, governed capability focused on verified business outcomes.

Unified Engineering Context

A persistent, centrally managed knowledge layer shared by every agent. It combines business context, architecture, standards, and engineering conventions to ensure consistent delivery, reduce context fragmentation, and keep generated outputs aligned with the intended system design.

Agentic Application Factory

The orchestrated delivery engine where refinement, execution, and verification are managed through a governed pipeline. Shared context, reviewed specifications, validation loops, and quality gates help reduce variability in AI-generated outputs and improve delivery predictability.

Governance, Security & Compliance

Built-in governance with human approval gates, policy-driven agent behavior, and continuous compliance validation. Every artifact is traceable from requirement to deployment, providing visibility, auditability, and organizational control across the software delivery lifecycle.

Supporting Platforms

Swifter.io

Tech Mahindra's enterprise-grade Agentic SDLC platform, providing pre-built workflows, full artifact traceability, and integrated AI agent teams. It supports GitHub, Azure DevOps, GitLab, Jira, Figma, and leading AI models and coding agents.

AppGinieZ™

Tech Mahindra's GenAI engineering platform that accelerates requirements refinement, test generation, code documentation, log analysis, and quality engineering activities. Together with Swifter.io, it supports both task-level automation and end-to-end agentic delivery.

lca-thumb

Platform-Agnostic Architecture

ASE works with your preferred AI, cloud, and DevOps ecosystem, enabling organizations to build on existing investments while using Swifter.io and AppGinieZ™ as optional accelerators.

What We Offer

Strategy & Consulting

Assess engineering readiness, define governance frameworks, and design operating models for human-agent collaboration. Deliver a practical roadmap for adopting agentic engineering with risk and compliance considerations built in.

Agentic Application Engineering Services

Implement and operate agentic capabilities across the software lifecycle, including orchestration, context engineering, telemetry, analytics, adaptive AI model management, and agent-driven DevOps practices.

Multi-Agent SDLC Orchestration

Coordinate specialized agents across requirements, architecture, development, testing, security, compliance, and operations through centralized orchestration and shared contextual intelligence.

Engineering Knowledge Intelligence

Connect requirements, designs, code, tests, and operational data into a unified knowledge fabric that improves traceability, change impact analysis, and knowledge reuse.

Agent-Driven DevSecOps

Automate testing, security validation, remediation, telemetry, and workflow orchestration to accelerate delivery while improving quality, reliability, and operational efficiency.

Business Impact

Measurable Outcomes

  • Up to 50% improvement in engineering productivity across development, testing, and DevOps.
  • 25–40% faster release cycles and 30–50% reduction in time-to-market.
  • Up to 80% SDLC task automation.
  • 30–50% reduction in defects, with up to 80% regression test automation and 95% deployment success rates.
  • 90%+ traceability coverage with continuous compliance validation and end-to-end auditability.

Proven Impact at Scale

For a global communications company, agentic practices were rolled out across 250+ developers, delivering:

  • 25–35% reduction in delivery timelines
  • 20–30% reduction in document generation effort
  • 30% effort savings in JUnit test case creation

Demonstrating how agentic engineering scales productivity across large enterprise delivery teams.

Explore More

TechM Consulting

Data & Analytics

Cloud and Infrastructure Services

Get In Touch

Need more information?  
We will take approximately 3-5 working days to respond to your enquiry.