Dyna Robotics is building reliable, commercially deployable general-purpose manipulation policies, not just demos.
“Robot Demos Are Easy. Reliability Is Hard”
reliability
16 tracked signals on reliability.
AI agents require distributed systems thinking because they now cause real-world side effects.
“we shouldn't ideally have uh allow AI agents to delete production databases.”
Agent reliability, not capability, is the critical unsolved problem for enterprise automation trust.
“if your agent one in four times deletes a database, you will never touch that agent again”
LangChain CEO outlines a five-stage agent lifecycle: build, test, deploy, monitor, govern
“getting something that works, you know, initially locally or in a one-off on a Twitter demo is easy, but like shipping it reliably to 100 of users at scale, the difficulty becomes down down to like the performance and the behavior of the agent”
As AI agents move to production, the core challenge shifts from model intelligence to running probabilistic agents on deterministic infrastructure.
“These systems are fundamentally probabilistic. Infrastructure is not allowed to be.”
AI agents routinely fabricate web searches, citations, and data when blocked by anti-bot defenses instead of admitting failure.
“There's no error, no warning, just the wrong answer.”
TypeSafe's Jev model uses RLCD to deliver reliable machine-native intelligence for software automation
70% of cloud outages are change-related; Azure now uses AI/ML and chaos engineering to detect and resolve failures without humans.
“70% of outages in the cloud and kind of at the industry scale are change related in some way.”
Traditional retry and circuit breaker patterns fail for LLMs; per-request provider fallback is required.
“If you have a single model provider, their ceiling is your ceiling. Their outage is your outage.”
NVIDIA Dynamo's shadow engine recovery restores LLM inference capacity in seconds instead of minutes
Agent loops amplify LLM hallucinations by compounding errors across downstream steps
“one hallucinated fact in step two can poison step three, step four and everything downstream”
AI agent demand is causing 10-100x user growth that database infrastructure cannot keep pace with
“the number of daily active users has doubled, tripled, 10 or 100 times. This is largely driven by the demand for AI agents. The infrastructure simply cannot keep up with this development.”
LLM hallucinations stem from their probabilistic next-token nature and cannot be fully eliminated, only mitigated.
“This minimizes hallucination, but it doesn't eliminate it.”
At 10 million server scale, one-in-a-million daily failures happen 10 times per day
“if it's a one in million chance of happening in a day, that means it's happened 10 times today.”
Databricks RADAR uses anomaly detection to catch gray failures monitoring misses.
“Some of the most damaging outages are the ones your monitoring never flags”
A 2014 Azure storage bug causing infinite loops led to Microsoft's safe deployment policy
“Okay, this is good. Let me start pushing this out across the world.”