Dyna Robotics deploys Dyna-2.1 robot achieving 99.4% success rate in 24-hour autonomous production tasks
production-deployment
8 tracked signals on production-deployment.
LangChain's Managed Deep Agents bundles harness and infrastructure to simplify production agent deployment
“a task that used to take them weeks, to spin up a new agent for a researcher, now just takes a few hours by putting this all into the Deep Agents harness and deploying it onto LangSmith”
Amdocs and AWS deployed a multi-agent telco support system saving 61,000 hours of wait time at PLDT.
“I'm choosing the right model to the right task... using Claude for reasoning, but using the Amazon Nova for these fast queries. And this is reduce our tokens by 95%.”
Enterprise AI agents fail in production due to missing foundation controls: auth gateways, MCP servers, eval systems, and PII protection.
“most of the people are missing the gateway to make sure that agent can, you know, communicate to the gateway to get authenticated first with the right auth providers”
AI agents in production should be treated as full applications, not isolated components.
“The last thing you want is for one agent to be responsible for many things, risking hallucinations.”
Fanatics Betting and Gaming deployed multi-agent AI on AWS to handle 40+ support queries per two minutes
“As we scaled, we knew our support experience needed to evolve with us. We wanted to give our customers faster, more accurate answers while making sure we never compromised on responsible gaming or compliance. The goal was to build something that got better over time, not just bigger.”
LangChain launches Managed Deep Agents to handle production agent infrastructure at scale.
“this will make it far more easy than before to build powerful agents and actually run them at scale”
Databricks lead shares a framework for taking AI demos to production, warning model-first thinking causes failures.
“And every conversation started with let's choose the model, right?”