RAG Architecture Comparison
Understanding the Differences: Traditional RAG vs Agentic RAG vs Google File Search
Traditional RAG Flow
Linear, deterministic pipeline. Single retrieval pass with embeddings. Documents passed directly to LLM for generation.
Agentic RAG Flow
Agent decides retrieval strategy, iterates, refines results, implements reasoning loops.
Feature Comparison Matrix
| Feature | Traditional RAG Simple | Agentic RAG Advanced | Google File Search Hybrid |
|---|---|---|---|
| Retrieval Strategy | Single-pass embedding search | Multi-pass, adaptive retrieval with reasoning | Multi-source cloud search with ML ranking |
| Decision Making | No – fixed pipeline | Yes – Agent decides next steps | ML-driven relevance ranking |
| Data Sources | Single vector DB | Multiple tools/APIs (tools-use) | Google Drive, Gmail, Docs, Sheets, etc. |
| Iteration | None – one shot | Multiple refinement loops | Semantic search with filtering |
| Context Quality | Medium – may miss nuance | High – refines through reasoning | Very High – full document context |
| Latency | ~100-200ms (fastest) | ~500-2000ms (slower, iterative) | ~200-500ms (cloud dependent) |
| Cost | Low – minimal processing | Medium-High – multiple LLM calls | Low-Medium – API dependent |
| Hallucination Risk | Medium-High | Lower – validated through reasoning | Lower – verified against actual files |
| Setup Complexity | Simple – 2-3 components | Complex – tool definitions, agents | Medium – OAuth, Google Workspace setup |
| Scalability | Excellent – linear | Good – agent overhead | Excellent – Google infrastructure |
Performance & Capability Metrics
Traditional RAG
Best For: Simple retrieval tasks with single document collection
Architecture:
- Query Embedding (text→vector)
- Vector Similarity Search
- Document Retrieval (k-nearest)
- Context Augmentation
- Single LLM Generation Pass
Strengths
- Fast & predictable
- Simple to implement
- Deterministic output
- Cost-effective
Limitations
- Single retrieval pass
- Poor multi-step reasoning
- Embedding gaps
- Limited refinement
Agentic RAG
Best For: Complex queries requiring reasoning, multi-step refinement
Architecture:
- Agent Decision Layer
- Multi-Tool Integration
- Adaptive Retrieval Strategy
- Iterative Refinement Loop
- Reasoning & Validation
Strengths
- Intelligent iteration
- Multi-source integration
- Superior reasoning
- Error correction
Limitations
- Higher latency
- Complex implementation
- Multiple LLM calls
- Higher costs
Google File Search
Best For: Enterprise users with Google Workspace, cross-platform search
Architecture:
- Google Workspace Integration
- Multi-Service Indexing
- Semantic ML Ranking
- Context-Aware Retrieval
- Native Authentication
Strengths
- Enterprise ready
- Multi-source coverage
- Secure & compliant
- ML-optimized ranking
Limitations
- Requires Google Workspace
- Vendor lock-in
- Limited customization
- Privacy considerations
Selection Guide – When to Use Each
Traditional RAG
✓ Fast query responses needed
✓ Simple knowledge base
✓ Budget constraints
✓ Latency critical
Agentic RAG
✓ Complex reasoning
✓ Multi-system integration
✓ Quality over speed
✓ Validation workflows
Google File Search
✓ Google Workspace user
✓ Enterprise compliance
✓ Cross-platform search
✓ Security priority
Regulatory & Compliance Considerations
Traditional RAG
21 CFR Part 11 & EU Annex 11: Vector embeddings audit trail, retrieval logging, deterministic output validation. Risk: Difficult to explain LLM rationale for rejection.
Agentic RAG
GAMP 5 Aligned: Agent reasoning loops, tool audit trails, iterative validation steps. Supports ALCOA+ with detailed decision logs. Advantage: Explainable AI for regulatory documentation.
Google File Search
Google Security Model: Encrypted at rest/transit, SOC 2 Type II compliance. Challenge: Data residency for EU GMP, cloud audit requirements.