Table of Contents Why AI Agent Continuous Evaluation Quietly Falls Apart Pattern 1: Layered Golden Dataset Evaluation Pattern 2: Pre-Deploy Quality Gates in CI/CD for AI Agents Pattern 3: Multi-Judge Consensus for Prompt Regression Testing Pattern 4: Shadow Evaluation Against Live Traffic Pattern 5: Failure Replay Loops Pattern 6: Cost-Tiered Evaluation Budgets Pattern 7: Eval-Driven…
Table of Contents Why AI Agent Replanning Quietly Breaks in Production Pattern 1: Plan Invalidation Triggers (Replan vs. Retry) Pattern 2: Cost-Bounded Replanning Budgets Pattern 3: Cycle Detection for Agent Loop Prevention Pattern 4: Plan Diff Verification for Safe Plan Recovery Pattern 5: Hierarchical Planner-Executor Split for Multi-Step Agent Planning Pattern 6: Invariant Carryover Across…
Most teams hit the multi-agent AI orchestration wall the same way. A single agent works fine. They split it into three “specialists” because the architecture diagrams look cleaner. Six weeks later, an agent is calling another agent calling the first one in a loop nobody noticed until the OpenAI bill arrives. Multi-agent AI orchestration is…
AI observability is the difference between a 5% AI program and a 95% one. And almost nobody is doing AI observability right in 2026. We have audited 14 enterprise AI deployments at Velocity Software Solutions over the last six months. Twelve of them had a Datadog or New Relic dashboard with a thin AI observability…
Table of Contents The Demo Trap 5 RAG Failure Patterns That Kill Production Deployments What Actually Works: The Production RAG Checklist The Cost of Getting It Wrong Your Next Step The Demo Trap The demo went perfectly. The CEO asked a question about Q3 revenue projections, the RAG system pulled the right document, and the…
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