AI research
Decision-ready AI research for practical teams.
Decision-ready AI research on governance, RAG, agents, model strategy, evaluation, privacy, and enterprise AI adoption.
50Research notes
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2Active months
AI Agent Governance Metrics for 2026
A research note on the governance metrics teams should track when AI agents move from experiments into workflow automation.
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June 2026
19 notes
AI Agent Governance Metrics for 2026
A research note on the governance metrics teams should track when AI agents move from experiments into workflow automation.
AI Workflow Auditability Framework for 2026
A research framework for making AI-assisted workflows easier to audit, review, explain, and improve across teams.
Context Engineering Evaluation Framework for AI Teams
A research note on evaluating context engineering quality across prompts, retrieval, memory, source selection, and workflow outcomes.
Vector Databases and RAG in 2026: Smart Retrieval Architecture Guide
A practical RAG and vector database guide for teams building AI search, internal knowledge assistants, support copilots, and retrieval-backed LLM applications.
Prompt Engineering: Advanced Techniques and Patterns for 2026
Master advanced prompt engineering techniques including chain-of-thought, few-shot learning, role-based prompting, and optimization patterns for complex AI tasks.
Multimodal AI Adoption Trends in 2026
An analysis of how text, image, audio, video, and screen-aware AI tools are changing practical adoption across teams.
LLM Fine-Tuning Best Practices for 2026: When and How to Adapt Models
Comprehensive guide to fine-tuning large language models, including cost-benefit analysis, techniques, tools, and practical implementation patterns for teams.
AI Agent Readiness Framework for 2026
A practical framework for deciding whether a workflow is ready for AI agents, automation, and human-in-the-loop controls.
AI Search Reliability in 2026: What Teams Need to Know Before They Trust It
An in-depth analysis of AI search accuracy, hallucination risk, citation quality, and what reliability actually means for research and business workflows.
AI Model Pricing and Cost at Scale: A 2026 Framework for Teams
A structured analysis of AI model pricing, hidden costs, cost-at-scale patterns, and how teams can build financially sustainable AI stacks.
AI Tool Privacy and Enterprise Data Handling: What Organizations Must Understand in 2026
An in-depth analysis of how AI tools handle enterprise data, where privacy risks actually exist, and how organizations can make informed decisions about data handling before deploying AI at scale.
Open vs Closed AI Models in 2026: Which Strategy Wins for Teams?
An in-depth analysis of the tradeoffs between open and closed AI models across cost, control, performance, privacy, and long-term business risk.
Project Glasswing: Using AI to Secure the World's Critical Software
An in-depth look at Anthropic's Project Glasswing initiative, which leverages Claude Mythos Preview to identify zero-day vulnerabilities in critical infrastructure before adversaries can exploit them.
AI Governance Operating Model for 2026
A research note on building a practical AI governance operating model with ownership, review, policy, risk tiers, and measurement.
Enterprise RAG Evaluation Methods for 2026
A research note on evaluating retrieval-augmented generation systems for accuracy, source quality, coverage, and user trust.
Small Language Models and Edge AI in 2026
A research note on small language models, edge deployment, privacy, latency, and when smaller AI systems are better than frontier models.
Synthetic Data for AI Testing in 2026
A research note on using synthetic data to test AI workflows, protect sensitive information, and improve evaluation coverage.
AI Agent Monitoring and Observability in 2026
A research note on monitoring AI agents, tracking tool use, reviewing failures, and building observability into automated workflows.
Human-in-the-Loop AI Review Patterns for 2026
A practical research note on where human review still matters in AI workflows and how teams can design review patterns without slowing everything down.
May 2026
31 notes
AI Trust Metrics for Leaders and Teams
A research note on measuring trust in AI systems through reliability, transparency, control, user confidence, and business outcomes.
Enterprise AI Roadmap Planning for 2026
A research note on planning AI initiatives across tools, workflows, governance, budget, training, and measurable business value.
AI Output Quality Assurance for Business Workflows
A practical research note on ai output quality assurance for business workflows, with decision criteria, rollout patterns, risks, metrics, and next steps for teams evaluating AI in 2026.
AI Procurement Checklist for Practical Buyers
A research note on procurement checks for AI tools, covering security, privacy, pricing, integrations, support, and adoption risk.
Agent Observability Basics for AI Operations
A research note on monitoring AI agents through traces, logs, tool calls, outcomes, failures, and escalation patterns.
AI Assistant Memory Governance
A research note on managing AI assistant memory, personalization, retention, user control, and privacy expectations.
Open Model Risk Assessment for Product Teams
A research note on evaluating open models for privacy, licensing, safety, quality, support, and deployment control.
Data Retention Choices for AI Tools
A practical research note on data retention choices for ai tools, with decision criteria, rollout patterns, risks, metrics, and next steps for teams evaluating AI in 2026.
AI Change Management Patterns for Adoption
A research note on adoption patterns that help teams introduce AI tools with training, feedback, ownership, and measurable outcomes.
Evaluation Scorecards for LLM Applications
A research note on building scorecards for LLM apps using accuracy, usefulness, safety, latency, cost, and review effort.
Role-Based AI Access Controls for Enterprise Adoption
A research note on using role-based access controls to manage who can use AI tools, models, data sources, and integrations.
AI Automation Boundary Design for Safer Workflows
A practical research note on ai automation boundary design for safer workflows, with decision criteria, rollout patterns, risks, metrics, and next steps for teams evaluating AI in 2026.
Knowledge Base Readiness for AI Assistants
A research note on preparing help centers, internal docs, and knowledge bases for AI retrieval and assistant workflows.
AI Product Analytics Metrics That Actually Matter
A research note on measuring AI product usage, quality, latency, cost, review load, retention, and task success.
Private AI Deployment Tradeoffs for Enterprise Teams
A research note on private AI deployment choices, including security, cost, latency, model quality, and operational complexity.
AI Meeting Intelligence Governance for Teams
A research note on meeting transcripts, summaries, action items, consent, retention, and knowledge reuse controls.
Prompt Library Maintenance for Repeatable AI Work
A practical research note on prompt library maintenance for repeatable ai work, with decision criteria, rollout patterns, risks, metrics, and next steps for teams evaluating AI in 2026.
AI Tool Vendor Risk Scoring for Buyers
A research note on scoring AI vendors by privacy, security, reliability, pricing, integrations, support, and governance controls.
Multimodal Review Workflows for Images, Video, and Documents
A research note on reviewing multimodal AI outputs across text, images, video, documents, and brand-sensitive content.
Vector Database Cost Management for RAG Teams
A research note on vector database cost drivers, indexing choices, storage growth, retrieval design, and operational controls.
Small Team AI Governance Without Heavy Process
A research note on lightweight AI governance for startups and small teams that need clarity without enterprise bureaucracy.
AI Browser Workflow Risk and Permission Design
A research note on browser-based AI assistant risks, permissions, page context, data exposure, and workflow controls.
Synthetic Test Sets for AI Tool Evaluation
A research note on using synthetic test sets to compare AI tools, check regressions, and evaluate quality before rollout.
AI Data Classification for Prompts and Context
A research note on adapting data classification frameworks for prompts, uploads, retrieval context, and AI tool integrations.
AI Cost Allocation Models for Growing Teams
A research note on how organizations can assign, monitor, and manage AI costs across teams, tools, models, and workflows.
Human Review Queues for AI Outputs
A research note on review queues, approval paths, and quality gates for AI-generated work in business workflows.
AI Agent Handoff Patterns for Human-Controlled Workflows
A research note on designing AI agent handoffs so automation can pause, escalate, and transfer work to humans safely.
RAG Source Quality Scoring for Reliable AI Answers
A research note on how source quality scoring can improve retrieval augmented generation and reduce weak or unsupported AI answers.
AI Model Routing Architectures for Cost and Quality
A research note on model routing patterns that send tasks to different AI models based on cost, risk, latency, and quality needs.
Enterprise Prompt Governance: Why Shared Rules Matter
A research note on prompt governance, reusable prompt libraries, sensitive data rules, and quality review for enterprise AI adoption.
AI Workflow Evaluation Framework for Practical Teams
A practical research note on ai workflow evaluation framework for practical teams, with decision criteria, rollout patterns, risks, metrics, and next steps for teams evaluating AI in 2026.