Research on AI agent simulation, reliability, and production monitoring.
Evidence-led writing for teams testing long-running agents, comparing complete trajectories, and improving autonomous workflows before and after deployment.
AI SDR testing
8 articles
How to test an AI SDR before launch
A practical release framework for testing the real outbound agent across complete buyer journeys, business outcomes, stops, and handoffs.
AI SDR simulation versus transcript evaluation
Transcript review explains what happened in one past conversation. Simulation tests how an agent behaves as buyers, time, tools, and consequences change.
How to compare two AI SDR versions
Use paired buyer worlds to separate real behavioral changes from audience noise, then inspect the trajectories behind every important delta.
How to test multi-day AI SDR sequences
Test delayed replies, follow-up timing, memory, objections, scheduling, stops, and handoffs as one stateful buyer journey.
Production monitoring for AI SDRs
Monitor version-scoped outbound trajectories against a reviewed baseline, investigate drift, and carry confirmed failures into the next release cycle.
How to measure an AI SDR funnel
Define evidence-backed stages from meaningful reply to booked meeting, then trace every stalled cohort to the workflow that produced it.
Testing AI SDR opt-outs and human handoffs
Verify stop signals, exclusions, suppression, escalation, and context transfer across the full outbound workflow, not just one response.
Turn AI SDR production failures into regression tests
Convert confirmed live failures into reviewed, privacy-minimized scenarios that every candidate agent must face before release.