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Module 5: Data & Knowledge Strategy

🧱 Module Purpose

To define the data infrastructure and informational boundaries for the AI agent. This includes identifying and structuring data sources, ensuring data quality and compliance, and explicitly stating what the agent should and should not attempt. A robust data strategy is foundational for agent reliability, accuracy, and trust.

🔍 Sub-Components & Templates

1. Data Source Inventory

Document all data sources needed for core agent functionality:

  • Internal systems (CRM, support platforms, databases)
  • External APIs and public datasets
  • File/document repositories (PDFs, Google Drive, SharePoint)

Source: Gemini + Perplexity data readiness assessments

2. Data Readiness Framework

Assess the quality, availability, and compliance of each data source:

  • Freshness and update frequency
  • Access controls and authentication
  • Sensitivity and compliance (GDPR, CCPA)

Tools: Schema validation, null value scans, privacy scoring

3. Data Pipeline Design

Plan how data will be ingested, transformed, and fed to the agent:

  • ETL/ELT pipelines
  • Chunking for RAG (text splitting, embedding)
  • Live vs. batch updates

Tools: LlamaIndex, LangChain, Airbyte, Prefect

4. Knowledge Indexing Strategy

Decide how information will be structured and queried:

  • Vector stores (Pinecone, FAISS, Weaviate)
  • Hybrid search (semantic + keyword)
  • Metadata tagging and filters

Critical for Retrieval-Augmented Generation (RAG)

5. Capability Boundaries Definition

Define what the agent should NOT attempt:

  • Unsupported data types (e.g., image/video if not configured)
  • Domains outside scope
  • Escalation paths for ambiguous or risky requests

Ensures safe, reliable, and explainable behavior

6. Failure Mode Analysis & Escalation Triggers

Prepare for edge cases and system errors:

  • What happens if data is missing?
  • When does the agent escalate to a human?
  • What fallback messages are used?

Source: Responsible AI and MLOps safety practices

7. Data Governance & Compliance Planning

Ensure data handling aligns with laws and internal policies:

  • Consent handling, PII controls
  • Anonymization, retention, audit logs
  • Third-party data licensing review

Source: CCPA/GDPR templates + Microsoft Purview / AWS Lake Formation

📈 Success Metrics

  • Data Quality Score (completeness, freshness, structure)
  • RAG Retrieval Relevance Accuracy
  • Compliance Audit Pass Rate
  • Escalation Accuracy Rate (false negatives avoided)

🛠 Tool & Integration Suggestions

  • Data Connectors: Airbyte, LlamaHub, Zapier, APIs
  • Pipeline Tools: Prefect, Dagster, LangChain loaders
  • Vector Stores: Pinecone, Weaviate, Chroma, FAISS
  • Compliance: Microsoft Purview, AWS Macie, DataDog

📦 Reusable Templates Included

  • Data Inventory Spreadsheet
  • Readiness Scoring Matrix
  • Chunking & Embedding Planner
  • Capability Boundaries Matrix
  • Failure Mode Worksheet
  • Escalation Mapping Guide
  • Governance Checklist

🔄 Development Tracks Mapping

Track Flow Outcome
Weekend Warrior Use ready-made datasets or low-risk sources Lightweight RAG setup with safe fallback rules
Startup Custom pipeline for 1–2 internal systems + privacy checklist Validated data inputs and scoped capabilities
Enterprise Full governance model + layered indexing strategy Secure, compliant, scalable knowledge foundation

🔗 External References to Incorporate

🔁 Dependency Links

  • Input: System architecture and integration targets from Module 4
  • Feeds into: Module 6 (Interaction Design), Module 8 (Performance Evaluation), Module 9 (Deployment)