A strategic vision for deploying intelligent agents to solve engineering pain points in our Azure DevOps environment.
SM
by Snigdha Mulukutla
Current Engineering Challenges
Knowledge Silos
Critical information trapped in wikis, PRs, and tribal knowledge.
Inconsistent Processes
Knowledge gaps, onboarding challenges, and tech debt handling lack standardization.
Limited Visibility
Engineering health metrics require manual collection and analysis.
Remote Collaboration
Engineers struggle with asynchronous communication and coordination.
AI Agent Ecosystem
Knowledge Center Bot
Answers technical questions using internal documentation.
Onboarding Buddy
Guides new engineers through setup and knowledge transfer.
Story Generator
Creates consistent user stories with acceptance criteria from past learnings.
Tech Debt Remediator
Identifies outdated patterns, refactors and fixes inefficiencies.
Daily Summary Bot
Delivers PR and build summaries to Microsoft Teams.
Knowledge Center Bot
Capabilities
Indexes all internal documentation
Understands code repositories
Answers technical questions
Provides relevant examples
Impact
Reduces search time
Breaks down knowledge silos
Improves accuracy of solutions
Speeds up decision-making
Onboarding Buddy & Story Generator
Onboarding Buddy
Proactively assists new engineers with environment setup. Shares tribal knowledge. Tracks progress through onboarding milestones.
Story Generator Agent
Learns patterns from historical stories. Creates consistent user stories with acceptance criteria. Suggests appropriate tags and estimates.
Combined Benefits
Reduces onboarding time from weeks to days. Improves story quality and consistency. Frees senior engineers from repetitive tasks and documents changing landscape.
Tech Debt Remediator
Detect
Scans repositories for outdated patterns and libraries
Document
Creates detailed tech debt items in Azure Boards
Suggest
Proposes remediation approaches and code samples
Track
Monitors progress and prioritizes based on impact
Daily Summary & Engineering Health
Collect
Gathers data from Azure DevOps and CI/CD pipelines
Analyze
Identifies patterns, bottlenecks, and success metrics
Alert
Notifies teams of failures or concerning trends
Report
Delivers daily summaries and weekly health reports
Implementation Timeline & Impact
This timeline outlines the three phases of AI agent implementation, highlighting the focus and agents deployed in each phase, with milestones and projected business impact.