Thinking about deterministic AI
Deep dives into decision infrastructure, governance patterns, auditability, and building agent systems you can trust in production.
Why SaaS AI Features Fail Enterprise Security Reviews (And How to Fix Them)
Catalogs eight common reasons SaaS sales stall at enterprise security review because of AI features. For each gap: the security questionnaire language that surfaces it and the architectural fix.
Memrail EMU ATOM architectureEMUs, ATOMs, and the Decision Plane: A Field Guide to Memrail's Core Abstractions
Technical reference for the three core abstractions of the Memrail Decision Plane: EMUs (policy evaluation units), ATOMs (typed memory facts), and the Decision Plane runtime. Shows how they implement propose-then-decide.
EU AI Act Article 13 complianceEU AI Act Article 13 in Practice: Building Transparency-Ready AI Systems
Translates Article 13 requirements into engineering specifications. Maps each sub-requirement to technical artifacts: system cards, decision traces, audit logs, policy documentation. Covers deployer vs provider split.
OpenAI Agents SDK governanceGoverning OpenAI Agents SDK Workflows: SDK-Native Guardrails vs Decision-Plane Authority
Architectural comparison of SDK-native guardrails (content filtering) versus decision-plane authority (policy enforcement, delegation control, audit). Covers the silent handoff risk and provides a decision matrix.
AI policy canary deploymentCanary Deployments for AI Policy Rules: How Safe Rollout Works in Practice
Introduces the draft, shadow, canary, active lifecycle for AI governance rules. Explains shadow diff analysis, rule versioning, immutable snapshots, and rollback safety with a worked expense approval example.
AI governance SaaS companiesAI Governance for SaaS-50 Companies: The Decision Infrastructure Every Scaling Team Needs
SaaS companies at the 50-to-500-seat scaling stage face enterprise customers demanding SOC 2 evidence, audit logs, and policy controls. Maps four SaaS AI architectures to governance requirements.
AI decision tracesDecision Traces Explained: What They Capture, Why They Matter, and How to Read Them
Defines what a complete decision trace must contain, distinguishes traces from generic application logs, and walks through a realistic trace example. Covers common gaps and trace reachability.
LangGraph deterministic AILangGraph and Deterministic AI: A Migration Guide to Governed Agent Workflows
A practical migration guide for teams with LangGraph in production who need to add governance without rebuilding. Covers five uncontrolled execution points and a staged migration: assessment, injection points, shadow-mode validation, full enforcement.
churn prevention automationChurn Prevention at Scale: Building Decision Protocols That Actually Keep Customers
Distinguishes churn prediction from churn action. Shows why most churn prevention programs fail and walks through a practical template: win-back triggers, priority sequencing, cooldown governance, and logged outcomes.
multi-agent governanceMulti-Agent Governance: Who Is Responsible When Agents Delegate to Agents?
Examines the accountability gap in multi-agent architectures and proposes three governance patterns: explicit delegation tokens, cascading trace propagation, and unified decision plane evaluation across agent boundaries.
SOC 2 Type II AI systemsSOC 2 Type II for AI Systems: What Auditors Actually Examine
Explains what SOC 2 Type II means for AI systems — which Trust Services Criteria apply, what evidence auditors collect for AI decision-making, and where AI-specific gaps commonly appear in existing controls.
AI rule reachability analysisThe Reachability Problem: Why Some Agent Rules Never Fire (and What to Do About It)
Addresses rules that can never fire because of evaluation ordering, priority conflicts, unmet preconditions, or cooldown suppression. Introduces reachability analysis as static analysis for AI rule sets.
ATOMs EMUs AI decision architectureATOMs and EMUs: A Practical Mental Model for Governed AI Decision-Making
Explains ATOMs (typed, structured facts) and EMUs (Executable Memory Units: rules that evaluate facts and prescribe actions) as a general mental model for governed AI decision-making. Compares to OPA/Rego policy-as-code.
LangGraph governance integrationHow to Add Governance to an Existing LangGraph Agent Without Rewriting It
Practical guide for teams running LangGraph agents who need to add a governance layer without rewriting orchestration logic. Covers three integration patterns: decision gate middleware, policy sidecar, and trace exporter.
AI governance financial servicesGoverned AI in Financial Services: What Banks and Fintechs Must Get Right
Covers the specific governance obligations facing AI systems in financial services: SR 11-7 model risk management, explainability requirements for credit decisions, SEC guidance on algorithmic advice, and EU AI Act classification.
LLM observability vs decision monitoringLLM Observability Is Not Enough: The Case for Decision-Level Monitoring
Distinguishes LLM-level observability (token usage, latency, prompt/response logs) from decision-level monitoring (which rules fired, what action was authorized, what was executed). Explains why LLM observability tools alone cannot answer compliance questions.
AI rule change managementBuilding a Rule Change Process for AI Systems: From Ad-Hoc Edits to Governed Rollout
Provides a step-by-step governance model for managing rule changes in AI production systems. Covers four stages: authoring and peer review (draft), validation against live traffic (shadow), limited-scope enforcement (canary), and full promotion (active).
SaaS billing automation failuresSaaS Billing Decision Failures: The Hidden Cost of Unsequenced Enforcement
Examines how billing automation breaks when enforcement decisions lack sequencing, priority rules, and cooldowns — leading to mass suspensions without warning, payment retry storms, and churn caused by the billing system itself.
decision plane vs orchestration layerDecision Plane vs. Orchestration Layer: Why Your Agent Framework Is Not Your Governance
Draws a precise architectural distinction between the orchestration layer (sequencing, routing, tool calls) and the decision plane (policy evaluation, authority, audit). Shows why conflating them creates brittle governance.
EU AI Act logging requirementsThe EU AI Act Compliance Gap: What High-Risk AI Systems Must Log
Breaks down what Article 12 of the EU AI Act actually requires high-risk AI systems to log: automatic recording of events, traceability of inputs/outputs, human oversight indicators, and operational data retention.
AI agent authority modelAgent Authority Models: Who Decides What Your AI Can Do?
Examines four authority model patterns (flat, hierarchical, delegated, coalition) that determine what AI agents can do and on whose behalf. Covers how authority is asserted, verified, and revoked across multi-agent systems.
SaaS customer lifecycle automationThe SaaS Lifecycle Decision Stack: How Modern Companies Automate Every Customer Touchpoint
Maps the five lifecycle stages (Acquire & Activate, Engage & Retain, Expand & Monetize, Billing & Revenue, Recover & Win Back) as a decision stack, explaining what decisions must fire at each stage and what happens when they are skipped.
AI agent production failuresWhy AI Agents Fail in Production: The Six Root Causes
Synthesizes six repeating patterns behind real-world AI agent failures — goal misgeneralization, context drift, tool boundary violations, cascading approvals, missing audit trails, and unsafe rule changes — with diagnostic questions for each.
AI governance financial servicesHow Financial Services Teams Are Governing AI Decisions in 2026
Financial services demands reproducibility, auditability, explainability, and safe change management. This article surveys how banks, insurers, and fintechs are governing AI decision systems.
safe rollout SaaS rulesSafe Rollout for SaaS Decision Rules: How to Change Live Business Logic Without Breaking Customers
Changing a live business rule carries real production risk. This article introduces safe rollout as a first-class concept for business logic: Draft, Shadow, Canary, Active.
AI agent authorization modelHow to Build an AI Agent Authorization Model Without Writing a Policy Engine from Scratch
Authorization determines whether your AI agents are deployable in production. This practical guide covers the three authorization primitives and walks through implementation using Cedar.
EU AI Act transparency requirementsThe EU AI Act's Article 13 Problem: What 'Transparency' Actually Requires from Your AI System
August 2, 2026 is the compliance deadline for high-risk AI systems under the EU AI Act. Most teams do not know whether their system qualifies or what Article 13 actually demands in practice.
AI decision audit logDecision Traces: The Audit Log Pattern That Makes AI Systems Defensible
When an AI system makes a consequential decision, someone will ask "why did it do that?" Teams without decision traces cannot answer. Those with decision traces answer in seconds.
decision infrastructure vocabularyATOMs, EMUs, and the Decision Plane: A Vocabulary for AI Decision Infrastructure
The field of deterministic AI decision infrastructure lacks a shared vocabulary. This article establishes working definitions for ATOMs, EMUs, Decision Plane, Decision Traces, Safe Rollout, and Reachability.
policy engine AI agentsOPA, Cedar, or Custom? Choosing the Right Policy Engine for Your AI Agents
A clear, opinionated comparison of the three dominant policy engine approaches for AI agent authorization: OPA with Rego, AWS Cedar, and custom rules engines. Includes a decision matrix and the honest 2026 recommendation.
SaaS workflow automationThe 5 SaaS Workflows Most Broken by Undocumented Decision Logic
Every SaaS company has decisions that live nowhere: in Slack threads, in institutional memory, in dead PRs. This article maps the five workflows where undocumented logic causes the most damage.
AI agents fail productionWhy AI Agents Fail in Production (And What the Architecture Is Missing)
Only 5% of enterprise AI systems reach production. This article diagnoses the real reasons — not model capability, but five structural architectural gaps that deterministic decision infrastructure can fix.
deterministic AI decisionsInfrastructure for Deterministic AI Decisions
A complete guide to building decision infrastructure that makes high-stakes AI decisions explicit, auditable, and safe to change. Covers the propose-then-decide architecture, deterministic decision points, audit-grade logging, and safe rule rollout.
decision protocol SaaSWhat Is a Decision Protocol? The Concept Every SaaS Team Needs in 2026
SaaS teams encode product intent in code, docs, and people — none designed to be queried, tested, or rolled back. A decision protocol is the fourth container: a named, versioned, explicit record of business logic.
AI agent governance frameworkThe Agent Governance Stack: Four Layers Every Enterprise Needs Before Going to Production
Most enterprises treat AI governance as a compliance checklist. It is not — it is an architecture. This article introduces the four-layer governance stack every enterprise needs before deploying AI agents to production.
AI decision plane architectureSeparating Logic from Models: Why Your AI System Needs a Decision Plane
When teams build AI systems, they put decision logic inside prompts or model calls — fast to demo, impossible to govern. The decision plane concept offers a better model: all consequential logic in an explicit, testable, versionable layer.