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.
All articles
16 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.
The agent passed the demo. Then it lost the thread.
Model capability has moved quickly. Operational proof has not. The difficult work now lives between one impressive response and a reliable outcome.
Simulation infrastructure is a painkiller, not a research accessory
Production is the most expensive place to discover how an autonomous system behaves under pressure. Simulation moves that learning earlier.
Regression is not just a code problem
A model swap, tool change, or context policy can alter behavior without touching the business logic. Compare systems in the same world, not against different anecdotes.
The telemetry you need is not a single score
Scores decide whether to look. Trajectories explain what changed, who felt it, and which engineering lever can fix it.
Physical AI needs more than a photorealistic world
Better world models and vision-language-action systems raise the ceiling for robotics. They also make behavioral simulation more urgent.
Context engineering needs a world, not a longer prompt
The agent's context is an internal view. Reliability depends on whether that view remains aligned with a changing external world.
The industry is building scoreboards. Autonomous systems need worlds.
Static datasets tell you what a model knows. Operating worlds reveal what an autonomous system does after its action changes the situation.
Synthetic users are useful only when they disagree
Volume is easy. The value comes from credible variation in incentives, memory, constraints, and reactions across a customer population.