001/INSIGHTS · FIELD NOTES
Field notes.
From production.
> Practical articles on architecture, agent coordination, memory, observability, and the patterns that show up again and again when you ship autonomous agent systems into production.
> 56 articles · written by the Hendricks team.
$ ls /insights/
56 filesArchitectureTask Handoff Failures: Why AI Agents Drop Work Between SystemsApril 2026 →
Data ArchitecturePartition Pruning Strategies for AI Agent Query Performance in BigQueryApril 2026 →
ArchitectureAgent Orchestration Patterns: Sequential vs Parallel vs Hierarchical Execution ModelsApril 2026 →
Data ArchitectureData Lineage Tracking in AI Agent Systems: Building Audit Trails from BigQuery to Business DecisionsApril 2026 →
ArchitectureBatch Processing vs Stream Processing in AI Agent ArchitecturesApril 2026 →
ArchitectureTime-Based Agent Activation Patterns: Scheduling AI Operations Without Human TriggersApril 2026 →
AI StrategyPattern Recognition vs Rule-Based Logic: Why AI Agents Excel Where Traditional Automation FailsApril 2026 →
EngineeringTransaction Isolation Levels for Multi-Agent Systems: Preventing Data Corruption in Concurrent OperationsApril 2026 →
ArchitectureResource Contention Patterns in Multi-Agent Systems: Preventing BigQuery Slot StarvationApril 2026 →
Data ArchitectureSchema Evolution Strategies for AI Agent Systems: Managing Data Model Changes in ProductionApril 2026 →
ArchitectureCheckpoint Patterns for Long-Running AI Agent Tasks: Preventing Complete Re-execution on FailureApril 2026 →
ArchitectureAgent Collision Detection: Preventing Duplicate Work When Multiple AI Agents Target the Same Operational TaskApril 2026 →
EngineeringMemory Leak Patterns in Long-Running AI Agent Systems: Detection and Prevention in BigQuery-Backed ArchitecturesApril 2026 →
ArchitectureStateful vs Stateless AI Agent Design Patterns for Production SystemsApril 2026 →
ArchitectureDependency Mapping for AI Agent Systems: Understanding Task Sequencing and Failure CascadesApril 2026 →
Data ArchitectureQuery Cost Optimization Patterns for AI Agent BigQuery WorkloadsApril 2026 →
ArchitectureConsensus Mechanisms for Multi-Agent Decision Making in Production SystemsApril 2026 →
EngineeringRetry Logic and Exponential Backoff Patterns for AI Agent Systems in ProductionApril 2026 →
Data ArchitectureCache Invalidation Strategies for AI Agent Decision Systems in BigQueryApril 2026 →
ArchitectureIdempotency Patterns for AI Agent Operations: Ensuring Safe Retries in Production SystemsApril 2026 →
EngineeringDead Letter Queue Patterns for Failed AI Agent TasksApril 2026 →
ArchitectureGraceful Degradation Patterns for AI Agent Systems: Maintaining Business Continuity When Models FailApril 2026 →
EngineeringVersioning and Rollback Strategies for Production AI Agent SystemsApril 2026 →
Workflow EngineeringOperational Handoff Protocols: How AI Agents Transfer Work Between Human and Machine TeamsApril 2026 →
PerformanceService Level Objectives for AI Agent Systems: Defining Uptime, Response Time, and Accuracy TargetsApril 2026 →
ArchitectureRate Limiting and Throttling Patterns for External API Calls in AI Agent SystemsApril 2026 →
Data ArchitectureSignal Degradation in Multi-Agent Systems: Why Clean Data Architecture Prevents Cascading FailuresMarch 2026 →
ArchitectureCircuit Breaker Patterns for AI Agent Systems: Preventing Cascade Failures in ProductionMarch 2026 →
ArchitectureError Recovery Patterns in Production AI Agent Systems: Building Self-Healing OperationsMarch 2026 →
PerformanceDecision Latency in AI Agent Systems: Why Response Time Determines Production ViabilityMarch 2026 →
ArchitectureAgent-to-Agent Communication Patterns: Building Self-Coordinating AI SystemsMarch 2026 →
ArchitectureSignal Pattern Libraries: Pre-Built Detection Logic for AI Agent Decision MakingMarch 2026 →
ArchitectureWhy AI Agents Need Event-Driven Architectures for Real-Time OperationsMarch 2026 →
ArchitectureAgent State Management: Why AI Systems Need Persistent Context Across SessionsMarch 2026 →
Data ArchitectureWhy BigQuery Should Be Your AI Agent's Memory, Not Just Your Data WarehouseMarch 2026 →
ArchitectureThe Hidden Cost of Skipping Architecture: Why AI Agent Sprawl Creates Technical DebtMarch 2026 →
ArchitectureWhat the A2A Protocol Means for Your Business OperationsMarch 2026 →
AI StrategyFrom RPA to AI Agents: The Migration Playbook for OperationsMarch 2026 →
EngineeringBuilding Agent Systems on Google Cloud: ADK, Agent Engine, and GeminiMarch 2026 →
GovernanceAI Agent Governance: The Architecture Layer Most Companies SkipMarch 2026 →
ArchitectureHow Multi-Agent Orchestration Replaces Manual WorkflowsMarch 2026 →
AI ImplementationWhy 89% of AI Agent Projects Never Reach ProductionMarch 2026 →
ResearchWhy 'More AI Agents' Is Not the Answer: What Google's Research Reveals About Scaling Intelligent SystemsMarch 2026 →
ArchitectureWhat Is Operating Architecture?March 2026 →
OperationsSigns Your Operations Need Architecture (Not More Tools)March 2026 →
AI ImplementationWhy Do AI Pilots Fail at Mid-Market Companies?February 2026 →
Data ArchitectureHow to Build a Data Foundation for AI in Your BusinessFebruary 2026 →
AI StrategyShould Mid-Market Companies Build AI In-House or Outsource?February 2026 →
PerformanceHow to Measure AI ROI: A Framework for Mid-Market LeadersFebruary 2026 →
IndustryHow AI Agents Are Transforming Professional Services OperationsFebruary 2026 →
Operating ArchitectureWhy Architecture Must Precede AutomationFebruary 2026 →
AI ImplementationThe Difference Between AI Experimentation and AI TransformationFebruary 2026 →
PerformanceMeasuring What Matters: Performance Metrics for Mid-Market LeadersJanuary 2026 →
Workflow EngineeringFrom Fragmented Tools to Unified ArchitectureJanuary 2026 →
IndustryOperating Architecture for Professional Services FirmsDecember 2025 →
Operating ArchitectureThe Five Layers of Intelligent Operating ArchitectureDecember 2025 →
999/PUT THESE INTO ACTION
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> Hendricks designs and installs the operating architecture behind every idea on this page.