After extensive analysis, I've identified the core patterns that underpin virtually every AI application. Most AI systems use one or more of these patterns, either individually or which can be combined to create more sophisticated solutions.
The Five Core AI Patterns
1. Static Knowledge Assistance
AI helps answer questions from a fixed corpus of information. Can be conversational/interactive with back-and-forth dialogue or simple Q&A.
Examples:
- Company policy chatbot
- Documentation Q&A
- Knowledge base assistant
- Troubleshooting guides
- Tax filing assistance
- Onboarding wizards
2. Entity-Based Processing
AI processes data about one or more entities, either interactively or in batch mode.
Interactive mode: User queries specific IDs on-demand, AI responds in real-time (e.g., "summarize claim #12345" or "compare these 3 vendors")
Batch mode: AI processes many entities at scale, typically scheduled or triggered, results stored for later use (e.g., classify 10,000 documents overnight)
Can be informational (aggregating/comparing existing data) or predictive (recommending actions).
Examples:
- Claim summaries
- Patient records
- Account analysis
- Personalized product recommendations
- Vendor comparisons
- Competitive analysis
- Document classification pipelines
- Data enrichment
- Entity extraction
3. Generative/Creative Production
Creating new content rather than querying existing data. Typically requires human review and editing before use.
Examples:
- Marketing copy generation
- Code writing
- Synthetic data creation
- Product descriptions
- Training simulations
- Language learning content
- Practice scenarios
4. Real-time Monitoring & Decision Systems
AI watches data streams and either alerts on anomalies or takes automated actions. May operate autonomously for low-risk decisions, but typically includes human override capabilities.
Examples:
- Fraud detection
- Quality control
- Dynamic pricing
- System health monitoring
- Network security
5. Multi-Step Assisted Workflows
AI helps execute complex tasks with dependencies over multiple interactions with human checkpoints. AI maintains context and state across sessions.
Simple proven autonomy: AI can execute well-defined, low-risk steps automatically (e.g., sending calendar invites, creating standard documents)
Complex requiring verification: Human approval required for key decisions and non-routine actions
Examples:
- Research with iterative refinement
- Code development with reviews
- Travel planning with approvals
- Document compilation with feedback loops
- RFP responses
- Meeting scheduling
Implementation Dimensions
These characteristics apply across the core patterns and define how you configure and enhance your AI system:
1. Prompt Engineering
How you structure requests and frame problems.
Controls:
- Explanation depth
- Optimization criteria
- Temporal scope (historical vs predictive vs scenario planning)
- Comparison requirements
- Cross-domain synthesis
Determines output style, reasoning approach, and response format.
2. Governance & Validation
Rules, constraints, guardrails, and compliance checks applied to AI behavior.
Includes:
- Security/privacy controls
- Bias prevention
- Ethical considerations
- Quality assurance
- Exception handling
Example: "Don't discount a person's disability when filtering candidates", contract review for missing clauses, compliance checking.
Can apply to any pattern as an overlay.
3. Feedback Loops
Interactive refinement capability where AI learns from user responses.
- Applicable in interactive modes across patterns 1, 2, 3, and 5
- Not typically applicable to batch processing or real-time monitoring
- In pattern 5, feedback loops are a core requirement
4. Execution Mode
Proactive vs Reactive: Does AI wait for explicit requests or anticipate needs?
Interactive vs Batch: Real-time user-driven or scheduled bulk processing?
Background vs Foreground: Passive observation or active engagement?
5. Scope
Entity scope: Single entity vs multiple entities vs cross-entity patterns
Domain scope: Single domain vs cross-domain synthesis
Temporal scope: Current state, historical analysis, predictive forecasting, or scenario simulation
6. Autonomy Level
Fully automated execution vs human-in-the-loop checkpoints.
- Varies by risk level and complexity of decisions
- Can range from AI as advisor (human decides) to AI as executor (human monitors)
Real-World Systems: Combining Patterns
Most production AI systems combine multiple core patterns with specific implementation dimensions.
Example - Contextual Co-pilot/Augmentation
Patterns: 2 (user as entity) + 5 (multi-step assistance)
Implementation Dimensions: Proactive execution mode, background operation, continuous feedback loops
Examples: GitHub Copilot, Google Smart Compose, meeting transcription assistants
Example - Smart CRM Assistant
Patterns: 2 (account data) + 3 (draft emails) + 5 (follow-up workflows)
Implementation Dimensions: Interactive mode, governance rules for communication tone, feedback loops for learning preferences
Example - Intelligent Document Processor
Patterns: 2 (extract entities in batch) + 4 (flag anomalies) + 3 (generate summaries)
Implementation Dimensions: Batch execution, validation rules for data quality, multi-entity scope
A Note on "AI Agents"
"AI Agents" typically map to Pattern 5 (Multi-Step Assisted Workflows), though the term is used loosely in marketing.
How to Apply This Framework
When evaluating or designing an AI system:
- Identify the core pattern(s) - Which of the 5 patterns best describes what you're building?
- Define implementation dimensions - How will you configure execution mode, scope, and autonomy?
- Add governance layers - What guardrails and validation do you need?
- Plan for feedback - Where does human refinement add value?
This framework helps cut through AI hype and focus on practical implementation.
What patterns are you using in your AI projects?
I work with government agencies and organizations developing AI strategies. If you're evaluating AI initiatives or need help separating signal from noise in AI vendor proposals, let's talk.
This framework emerged from analyzing dozens of AI implementations across public sector and commercial applications. It provides a practical lens for understanding how AI systems actually work, beyond vendor marketing.