Deep Research Prompts
CODITECT Product Suite Development Focus Areas
Document Type: Research Agenda
Version: 1.0
Purpose: Guide deep-dive research to maximize CODITECT product value
Date: February 2026
Research Category 1: Ontology Architecture
Prompt 1.1: Healthcare Domain Ontology Design
Research the optimal ontology structure for healthcare workflow automation.
FOCUS AREAS:
1. Core object types required for healthcare operations (patient, encounter, claim, etc.)
2. Relationship types between healthcare entities
3. Temporal modeling for healthcare events (bi-temporal requirements)
4. FHIR R4 alignment vs. custom modeling tradeoffs
5. Ontology versioning strategies for healthcare schema changes
DELIVERABLES:
- Comprehensive object type catalog with properties
- Link type definitions with cardinality rules
- Mapping to FHIR resources where applicable
- Schema evolution strategy document
- Comparison with Palantir's healthcare ontology patterns (where publicly known)
CODITECT APPLICATION:
- Define CODITECT's healthcare domain model
- Ensure interoperability with EHR systems
- Enable AI agents to reason about healthcare entities
Prompt 1.2: Financial Services Ontology Design
Research ontology patterns for financial services compliance automation.
FOCUS AREAS:
1. Regulatory entity modeling (accounts, transactions, customers, filings)
2. Anti-money laundering (AML) workflow object types
3. KYC (Know Your Customer) data relationships
4. Audit trail requirements for financial regulators
5. Cross-jurisdictional compliance modeling
DELIVERABLES:
- Financial services object type catalog
- Compliance workflow patterns
- Regulatory mapping (SEC, FINRA, OCC, state regulators)
- Audit trail specification for financial transactions
- Comparison with existing financial ontology standards (FIBO, etc.)
CODITECT APPLICATION:
- Define CODITECT's financial services domain model
- Enable compliance automation agents
- Support multi-jurisdictional requirements
Prompt 1.3: Ontology Interoperability Research
Research how to build ontologies that interoperate with existing enterprise systems.
FOCUS AREAS:
1. Open standards for ontology representation (JSON-LD, OWL, RDF)
2. Mapping between proprietary and open formats
3. Import/export patterns for ontology data
4. Federation strategies across multiple ontologies
5. Vendor lock-in prevention mechanisms
DELIVERABLES:
- Ontology format specification for CODITECT
- Import/export adapters for common formats
- Federation architecture for multi-system environments
- Lock-in risk assessment and mitigation strategies
CODITECT APPLICATION:
- Differentiate from Palantir's closed architecture
- Enable ecosystem partnerships
- Reduce customer switching costs
Research Category 2: Agent Architecture
Prompt 2.1: Guardrail-Constrained Agent Design
Research optimal architectures for autonomous AI agents operating within regulatory constraints.
FOCUS AREAS:
1. Guardrail types (regulatory, business, security, resource)
2. Guardrail enforcement mechanisms (pre-execution, runtime, post-execution)
3. Human checkpoint patterns for regulated decisions
4. Agent planning under constraints
5. Fallback strategies when guardrails block actions
DELIVERABLES:
- Guardrail framework specification
- Agent execution loop with constraint checking
- Human checkpoint API design
- Testing strategies for guardrail effectiveness
- Comparison with Palantir's "Agentic AI Hives" approach
CODITECT APPLICATION:
- Build compliance-safe autonomous agents
- Enable automation while maintaining regulatory defensibility
- Reduce human intervention for low-risk decisions
Prompt 2.2: Multi-Agent Orchestration Patterns
Research patterns for orchestrating multiple AI agents in enterprise workflows.
FOCUS AREAS:
1. Orchestrator-worker patterns (Anthropic patterns)
2. Agent communication protocols
3. Task decomposition strategies
4. Failure handling and recovery
5. Token budget management across agent hierarchies
6. Checkpoint and recovery mechanisms
DELIVERABLES:
- Multi-agent architecture specification
- Communication protocol design
- Failure mode analysis and recovery patterns
- Token economics model for multi-agent systems
- Performance benchmarks for different orchestration patterns
CODITECT APPLICATION:
- Scale automation beyond single-agent capabilities
- Handle complex workflows with multiple specialized agents
- Optimize cost while maintaining quality
Prompt 2.3: Agent Observability and Debugging
Research observability patterns for AI agents in production environments.
FOCUS AREAS:
1. Tracing agent decision-making processes
2. Logging strategies for agent actions and reasoning
3. Metrics for agent performance (success rate, latency, cost)
4. Debugging tools for agent failures
5. Explainability requirements for regulated industries
DELIVERABLES:
- Agent observability specification
- Logging schema for agent operations
- Dashboard design for agent monitoring
- Debugging toolkit requirements
- Explainability report templates for auditors
CODITECT APPLICATION:
- Enable production debugging of AI agents
- Meet regulatory explainability requirements
- Optimize agent performance over time
Research Category 3: Compliance Automation
Prompt 3.1: FDA 21 CFR Part 11 Implementation
Research technical implementation of FDA 21 CFR Part 11 compliance for AI-driven systems.
FOCUS AREAS:
1. Electronic signature requirements and implementation
2. Audit trail specifications (21 CFR 11.10(e))
3. System validation requirements (GAMP 5)
4. Access control requirements
5. Data integrity requirements (ALCOA+)
DELIVERABLES:
- Technical specification for 21 CFR Part 11 compliance
- Electronic signature API design
- Audit trail schema meeting FDA requirements
- Validation documentation templates
- Gap analysis: AI-specific compliance challenges
CODITECT APPLICATION:
- Enable AI agents in FDA-regulated environments
- Generate compliance artifacts automatically
- Reduce validation burden for customers
Prompt 3.2: HIPAA Technical Safeguards
Research HIPAA technical safeguard implementation for AI platforms handling PHI.
FOCUS AREAS:
1. Access control (45 CFR 164.312(a)(1))
2. Audit controls (45 CFR 164.312(b))
3. Integrity controls (45 CFR 164.312(c)(1))
4. Transmission security (45 CFR 164.312(e)(1))
5. AI-specific considerations (LLM prompts containing PHI)
DELIVERABLES:
- HIPAA technical safeguard implementation guide
- PHI detection and handling protocols
- Encryption specifications (at-rest, in-transit)
- Access control framework for healthcare data
- AI prompt sanitization strategies
CODITECT APPLICATION:
- Handle healthcare data safely in AI workflows
- Prevent PHI exposure in LLM operations
- Generate HIPAA compliance documentation
Prompt 3.3: Cross-Regulatory Compliance Harmonization
Research patterns for harmonizing compliance across multiple regulatory frameworks.
FOCUS AREAS:
1. Mapping between FDA/HIPAA/SOC2 requirements
2. Common control frameworks (NIST, ISO 27001)
3. Automation opportunities across frameworks
4. Conflict resolution between regulations
5. International regulatory mapping (GDPR, EMA)
DELIVERABLES:
- Cross-regulatory mapping matrix
- Unified compliance framework specification
- Automation coverage analysis by regulation
- Conflict resolution guidelines
- International expansion compliance roadmap
CODITECT APPLICATION:
- Enable multi-regulation compliance automation
- Reduce redundant compliance work
- Support international customer expansion
Research Category 4: Go-to-Market Innovation
Prompt 4.1: Bootcamp Model Optimization
Research optimization of the AIP Bootcamp model for regulated industries.
FOCUS AREAS:
1. Palantir's 5-day bootcamp structure and success factors
2. Compression opportunities for 2-day format
3. Customer data handling during bootcamps
4. Conversion rate optimization strategies
5. Scalability challenges and automation opportunities
DELIVERABLES:
- CODITECT Compliance Bootcamp playbook
- Day-by-day agenda with time allocations
- Data handling protocols for customer data
- Conversion rate benchmarks and targets
- Automation roadmap for bootcamp delivery
CODITECT APPLICATION:
- Compress sales cycle to 2 days
- Demonstrate value with customer data
- Scale bootcamp delivery without linear headcount
Prompt 4.2: ROI Quantification Framework
Research frameworks for quantifying AI automation ROI in regulated industries.
FOCUS AREAS:
1. Time savings measurement methodologies
2. Error reduction quantification
3. Compliance cost avoidance calculations
4. Productivity multiplier approaches
5. TCO (Total Cost of Ownership) comparisons
DELIVERABLES:
- ROI calculator specification
- Benchmark data for healthcare/financial automation
- Case study templates with quantified outcomes
- TCO comparison framework (CODITECT vs. manual vs. alternatives)
- ROI presentation templates for customer demos
CODITECT APPLICATION:
- Prove "20x ROI in 20 days" claim
- Enable self-service ROI calculation
- Support deal justification at customer organizations
Prompt 4.3: Mid-Market Positioning Strategy
Research positioning strategies for mid-market enterprise AI platforms.
FOCUS AREAS:
1. Mid-market buyer personas and decision criteria
2. Pricing models for $50K-$500K deals
3. Self-service vs. sales-assisted go-to-market
4. Competitive positioning against enterprise players (Palantir)
5. Channel partnership opportunities
DELIVERABLES:
- Buyer persona documentation
- Pricing strategy with tiered options
- Self-service onboarding specification
- Competitive battlecard vs. Palantir
- Channel partnership framework
CODITECT APPLICATION:
- Own the underserved mid-market
- Scale without enterprise sales headcount
- Build defensible position before Palantir expansion
Research Category 5: Technical Architecture
Prompt 5.1: LLM Orchestration Optimization
Research optimal architectures for multi-model LLM orchestration in enterprise environments.
FOCUS AREAS:
1. Model routing algorithms based on task characteristics
2. Prompt optimization for cost reduction
3. Caching strategies for repeated queries
4. Fallback patterns for model failures
5. Cost tracking and optimization
DELIVERABLES:
- Model routing algorithm specification
- Prompt engineering guidelines for efficiency
- Caching architecture for LLM responses
- Failover and degradation patterns
- Cost monitoring dashboard design
CODITECT APPLICATION:
- Achieve 40-70% token cost reduction
- Maintain quality while optimizing cost
- Enable transparent pricing for customers
Prompt 5.2: Deployment Flexibility Architecture
Research multi-environment deployment architectures for regulated industries.
FOCUS AREAS:
1. Kubernetes-based deployment patterns
2. On-premise deployment for healthcare/financial
3. Hybrid cloud architectures
4. Air-gapped deployment for future government
5. Configuration management across environments
DELIVERABLES:
- Deployment architecture specification
- Environment-specific deployment guides
- Configuration management strategy
- Security considerations by deployment type
- Operational runbooks for each environment
CODITECT APPLICATION:
- Meet regulated industry deployment requirements
- Achieve Apollo-like deployment flexibility
- Support hybrid customer environments
Prompt 5.3: Data Integration Patterns
Research data integration patterns for healthcare and financial services systems.
FOCUS AREAS:
1. EHR integration patterns (Epic, Cerner, Meditech)
2. FHIR API integration strategies
3. Claims/EDI integration (X12, HL7)
4. Real-time vs. batch integration tradeoffs
5. Data quality validation patterns
DELIVERABLES:
- Integration architecture specification
- Connector library requirements
- Data quality framework
- Error handling and reconciliation patterns
- Integration monitoring dashboard design
CODITECT APPLICATION:
- Enable rapid customer data connection
- Support bootcamp data integration
- Build connector library for key systems
Research Category 6: AI Safety and Trust
Prompt 6.1: AI Audit Trail Design
Research audit trail architectures that meet regulatory requirements for AI systems.
FOCUS AREAS:
1. Audit trail data model for AI decisions
2. Hash chain integrity mechanisms
3. Retention and archival requirements
4. Query and reporting capabilities
5. Tamper evidence and detection
DELIVERABLES:
- Audit trail schema specification
- Hash chain implementation design
- Retention policy framework
- Audit report generation templates
- Integrity verification tools
CODITECT APPLICATION:
- Meet FDA/HIPAA/SOC2 audit requirements
- Enable regulatory inspections
- Provide customer transparency
Prompt 6.2: AI Explainability for Regulated Industries
Research explainability requirements and techniques for AI in regulated environments.
FOCUS AREAS:
1. Regulatory explainability requirements by industry
2. LLM explanation generation techniques
3. Decision tree reconstruction from AI reasoning
4. Human-readable explanation formats
5. Explainability vs. performance tradeoffs
DELIVERABLES:
- Explainability requirements matrix by regulation
- Explanation generation specification
- Report templates for regulators
- Explainability testing methodology
- Performance impact analysis
CODITECT APPLICATION:
- Meet regulatory explainability mandates
- Build customer trust in AI decisions
- Enable audit and inspection readiness
Prompt 6.3: AI Model Governance
Research governance frameworks for AI models in enterprise production environments.
FOCUS AREAS:
1. Model versioning and lifecycle management
2. Model validation and testing requirements
3. Bias detection and mitigation
4. Performance monitoring and drift detection
5. Model retirement and replacement
DELIVERABLES:
- Model governance framework specification
- Validation testing protocol
- Bias detection methodology
- Monitoring dashboard requirements
- Model lifecycle management tools
CODITECT APPLICATION:
- Manage AI models safely in production
- Meet emerging AI governance regulations
- Build customer confidence in AI reliability
Research Prioritization Matrix
| Research Area | Business Impact | Technical Complexity | Priority |
|---|---|---|---|
| Healthcare Ontology | High | Medium | P0 |
| Guardrail Agents | High | High | P0 |
| FDA Compliance | High | Medium | P0 |
| Bootcamp Optimization | High | Low | P0 |
| ROI Framework | High | Low | P0 |
| Financial Ontology | Medium | Medium | P1 |
| Multi-Agent Orchestration | Medium | High | P1 |
| LLM Orchestration | Medium | Medium | P1 |
| Deployment Flexibility | Medium | High | P1 |
| Cross-Regulatory | Medium | Medium | P2 |
| Ontology Interop | Low | Medium | P2 |
| Agent Observability | Low | Medium | P2 |
| AI Explainability | Low | High | P2 |
Prompt Usage Guidelines
For Deep Research Tasks
- Use Research feature for prompts requiring >20 tool calls
- Provide context from this document and other artifacts
- Request structured output in markdown for documentation
- Validate findings against Palantir public materials
For Implementation Tasks
- Reference ADRs for architectural constraints
- Use TDD patterns as starting points
- Follow compliance requirements from research outputs
- Validate against ground truth (tests, compliance rules)
For Strategic Tasks
- Reference Executive Summary for context
- Use CODITECT Impact Analysis for positioning
- Validate against Palantir benchmarks
- Document decisions as new ADRs
Deep Research Prompts v1.0 — February 2026