During my Software Engineering Internship at Google, I worked on AI-assisted engineering workflows that helped teams organize technical reports, preserve context during collaborative investigations, analyze recurring user feedback, and improve software quality.

This case study focuses on the problems, design principles, and lessons behind the work. The diagrams are conceptual, the examples are fictional, and implementation details have been intentionally generalized.

Key contributions included:

• Prototyping a coordinated AI workflow that turns unstructured technical reports into clearer context for engineers.
• Improving continuity and collaboration for investigations that span multiple people or work sessions.
• Designing a two-stage feedback analysis process that establishes stable themes before matching newly arriving reports.
• Contributing maintainability improvements and automated quality checks for AI-assisted workflows.

Coordinated AI-Assisted Triage

Large engineering organizations receive complex technical reports that must be understood, connected to relevant context, and translated into a useful starting point for investigation. I explored a modular approach that divides this work across focused responsibilities while keeping an engineer in control of the final decision.

Orchestration layer

AI Orchestrator

Interprets requests, coordinates specialized agents, and synthesizes responses.

01

Knowledge Agent

Finds relevant information and system context.

• Intent parsing
• Query planning
• Knowledge retrieval
• Response synthesis

02

Investigation Agent

Plans multi-step analysis and gathers evidence.

• Investigation planning
• Evidence collection
• Hypothesis testing

03

Code Analysis Agent

Understands software structure and technical context.

• Source code reading
• Dependency exploration
• API & symbol search

04

Issue Triage Agent

Summarizes issues and identifies related reports.

• Symptom summarization
• Similar issue search
• Initial triage notes

05

Workflow Agent

Retrieves context and performs supported actions.

• Context retrieval
• Task execution
• Skill & test automation

Shared capabilities

• Documentation search
• Semantic search
• Knowledge base
• Session memory
• Tool execution

The conceptual workflow coordinates specialized components before presenting a concise handoff for human review:

• Context Framing: Organizes the reported symptoms and the most relevant supporting information.
• Evidence Organization: Connects related signals and highlights where further investigation may be useful.
• Human Handoff: Produces a structured summary that an engineer can verify, refine, or redirect.

This separation of responsibilities made the workflow easier to reason about, evaluate, and improve without treating AI output as the final authority.

Continuity & Collaboration

Engineering investigations often span interruptions, work sessions, and team handoffs. Losing the accumulated context forces people to reconstruct earlier decisions and evidence.

I focused on interaction patterns that make longer-running AI-assisted work more dependable:

• Context Continuity: Preserve the useful history of an investigation so work can resume without starting over.
• Structured Handoff: Make prior evidence, open questions, and decisions easy for another engineer to understand.
• Human Control: Keep AI suggestions inspectable and editable throughout the workflow.

Recurring User-Feedback Analysis

I also explored a recurring product challenge: high volumes of unstructured feedback make manual theme identification slow and inconsistent. The proposed workflow first clusters a historical backlog into a stable theme set, then compares only newly arriving reports against those themes during each recurring run. A new theme is created only when the incoming feedback does not fit an existing one.

01

Initial Theme Discovery

One-time or periodic batch

Existing Unclustered Feedback

Historical reports

Uploads stall at 99%. Large files never finish. Password reset keeps looping.

→

ML Clustering

Discover themes and group similar reports

→

Initial Theme Set

Stable clusters become shared context

File Upload Reliability Uploads stall · Large files fail

Account Access Password reset loops

Existing themes ↓

02

Daily Incremental Triage

Runs each morning

New Daily Feedback

Only newly arrived reports

My upload never completes. Search filters keep resetting.

→

Semantic Match

Compare against the existing theme set

→

Strong match My upload never completes. Append to Existing Cluster File Upload Reliability Add report · Refresh priority

No clear match Search filters keep resetting. Create New Cluster Search & Filter State Only when no existing theme fits

Both paths feed the engineering workflow ↓

01 Engineering Queue

→

02 Human Review

→

03 Investigation Summary

→

04 Continuous Learning

• Status & volume signals
• Re-cluster or split
• Update priority

↶ New evidence updates the initial theme set

The design emphasizes a stable taxonomy, incremental processing, and human oversight:

• Stable Baseline: Establish an initial set of themes from the historical feedback backlog.
• Incremental Matching: Compare new reports with existing themes before creating anything new.
• Human-Guided Correction: Let reviewers re-cluster or split themes when the grouping no longer reflects the underlying feedback.
• Priority Signals: Reassess attention as report volume and status signals change over time.

Maintainability & Evaluation

I also contributed to code health and long-term reliability:

• Code Quality: Simplified existing components and improved readability and maintainability.
• Reusable Patterns: Organized repeated workflow behavior into clearer, more consistent building blocks.
• Automated Evaluation: Added repeatable checks to catch unintended changes in AI-assisted behavior.

Reflection & Systems Insights

This work reinforced several lessons for building trustworthy AI-assisted engineering tools:

• Continuity Builds Trust: Preserving useful context matters when work unfolds over time or moves between people.
• Clear Responsibilities Improve Reliability: Smaller, well-defined components are easier to evaluate and refine.
• Human Oversight Is Part of the System: Review, correction, and escalation paths should be designed into the workflow.
• Evaluation Protects Quality: Repeatable checks are essential when AI behavior can change as the surrounding system evolves.