AI-Augmented Development Life Cycle

The Future of Software Development

Isolated AI tools don’t transform software delivery

Fragmented Across the Lifecycle

AI agents operate in silos instead of being embedded across requirements, development, testing, and release.

Not Connected to Delivery KPIs

Use cases don’t tie to measurable outcomes like throughput, cycle time, or quality.

Detached from Real Engineering Workflows

AI POCs don’t integrate into backlog execution, CI/CD automation, or release governance — limiting real delivery impact.

No Operating Model Change

Teams add AI tools but don’t redesign how workflows.

No End-to-End Orchestration Framework

There’s no system to coordinate AI agents, people, and processes across the SDLC.

How We Do That? - CI&T ADLC Platform

The CI&T ADLC Platform is an AI-agent–augmented delivery platform that orchestrates the end-to-end software development lifecycle — transforming requirements into structured backlogs, accelerating execution, and supporting teams from grooming through release.

• AI-Agent–Augmented End-to-End Delivery
• Seamless Integration with Jira and Bitbucket/GitHub
• Human Approval at Every Critical Step
• Enterprise-Ready Control, Traceability, and Transparency
• Team Performance Monitoring & Delivery Insights
• Accessible AI Adoption for Any Dev Team

CI&T AI AGENT HUB

Insight Agent

Discovery Agent

Analyzes requirements, identifies edge cases, and performs PRD deep-dives.

Grooming Agent

Optimization Agent

Continuously refines the product backlog, breaking down complex epics.

Planning Agent

Strategic Agent

Orchestrates multi-sprint roadmaps and handles dependency mapping.

Code Writing Agent

Engineering Agent

Generates production-ready code shards based on technical specifications.

Code Quality Agent

Review Agent

Enforces coding standards and performs automated peer reviews.

Code Security Agent

Security Agent

Proactively scans code for vulnerabilities and OWASP compliance.

Root Cause Analysis Agent

Diagnostics Agent

Investigates bugs and incidents by tracing neural execution paths.

Test Cases Agent

QA Design Agent

Synthesizes logical test scenarios from business requirements.

Automation Testing Agent

QA Automation Agent

Builds and executes end-to-end regression suites in CI/CD pipelines.

ADLC Portal

Authentication, Role& Permission

_{Authentication relies on the company’s SSO, and project roles and permissions strictly follow the existing Jira configuration to ensure consistent access control and prevent security risks.}

Configuration Per User

_{Personalized Integration & Security Control
Seamless Connectivity: Empower users to configure individual integrations with Jira, Bitbucket, and GitHub.
Enterprise-Grade Security: All authentications are handled via encrypted API tokens.
Privacy-First Design: Ensures end-to-end protection with encryption at rest, HTTPS transmission, and strict log masking.}

Requirement Grooming

_{Grooming Workbench, where an AI Agent analyzes uploaded project requirement and automatically generates structured user stories with tags and estimates. It helps teams quickly turn requirements into actionable backlog items and seamlessly sync or push updates to Jira.}

Release Planning

_{Planning Workbench, where the AI Agent supports release planning by organizing accepted user stories into a structured release scope and monitoring team capacity. It also integrates with Jira, allowing users to sync planning data and push updates directly to ensure alignment between AI-assisted planning and project delivery.}

Requirement Grooming

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