Interactive AI

Introduction

In this case study, I walk through how I approached the Discovery phase, decomposed requirements, structured workflows, and delivered both UX architecture and final UI for the Knowledge Base (KB) module of an Agentic IDE. This is one of the core sections of any agentic development platform because it defines how product teams ingest, organize, evaluate, and serve enterprise knowledge to AI agents and AI-powered applications.

Context

The goal was to create a scalable, transparent, and governable knowledge system that supports large organizations across multiple regions, strict RBAC rules, and demanding operational workflows.

The Knowledge Base enables users to:

Ingest & Connect — Upload documents or integrate 3P connectors (Google Drive, Notion, Confluence, S3, websites, databases).

Normalize & Index — Apply processing pipelines: chunking, embeddings, metadata extraction, language detection, deduplication, versioning.

Organize — Structure content into Collections with RBAC rules, labels, retention policies, PII tagging, and governance settings.

Evaluate & Monitor — Track retrieval quality (precision/recall proxies), freshness, gaps, cost and latency.

Serve to Agents & Workflows — Define retrieval policies, routing rules, synonyms, filters, and provenance rules for each assistant.

Roles involved

• AI/ML Engineer: Connects data sources, tunes retrieval, manages embeddings.
• Product Manager: Curates collections, ensures coverage, tracks quality.
• Knowledge/Data Ops: Oversees governance, PII, compliance, retention and access.
• Support/Success Lead: Ensures customer-facing assistants rely on correct and canonical knowledge.

Requirements & constraints

• Scale: Up to 500k docs, 50+ source types, multi-region storage with tenant isolation.
• RBAC: Org → Project → Collection → Document. ACL syncing from source systems when possible.
• Privacy & Compliance: PII tags, soft delete, data residency locks, full audit logs (view/export/embed history).
• Quality: Multiple chunking strategies, re-embedding triggers, stale content warnings.
• Ops: Sync health, retry logic, queues, rate limits, cost controls.
• Accessibility: Keyboard-first, screen reader compatible, safe contrast, dark mode.

Discovery phase

1. Activity decomposition by roles — Full decomposition of activities per role to understand who does what and at which granularity.
2. Mapping role relationships with metadata & content — Use-case diagram to visualize ownership boundaries, visibility rights, and operational touchpoints.
3. State-based workflow — Linear, state-based workflow capturing the entire lifecycle of a document inside the Knowledge Base.
4. Role-based process matrix — RACI-style process matrix with sequence layer for notification triggers, action required steps, responsibility ownership, and cross-functional dependencies.

UX architecture and userflows

Once the processes were validated, I mapped them to five core user activities:

1. Ingest & Connect
2. Normalize & Index
3. Organize Collections
4. Evaluate & Monitor
5. Serve to Agents/Workflows

Wireframing and UI

With functional clarity secured, I moved on to low-fidelity wireframes to validate layout logic, information hierarchy, and operational clarity — surfacing edge cases including re-embedding triggers, conflict resolution, stale version warnings, and cross-region data rules.

Before developing the visual language, I explored benchmarks such as OpenAI Playground, Glace, and Google Vertex Search to understand conventions in enterprise AI tooling.

The final UI includes:

• Connected Data Source example (Google Drive)
• Document revision history
• Sync status and retry logic
• PII tags and audit logs
• Embedding and chunking pipelines
• Retrieval policy builder
• Collection management dashboards

Outcome

The resulting Knowledge Base module provides:

• A complete lifecycle flow for enterprise content
• Transparent governance and auditable provenance
• Scalable and compliant multi-region storage
• Fine-grained retrieval tuning for agents
• A UX architecture aligned with the complexity of agentic systems

It serves as a foundational component of the Agentic IDE and significantly improves the reliability, quality, and explainability of AI-powered assistants across the platform.