Running AI in Production: Reliable AI Workflows, Governed Access, at Scale
Most groups constructing AI workflows attain for brokers and abilities, and find yourself with one thing probabilistic from finish to finish.
The agent calls no matter device it decides to name, reaches for an costly mannequin on a step that wanted a lookup, and runs as a black field: exhausting to validate, exhausting to foretell, exhausting to price out. This means a human has to examine each output, or AI will get narrowed to low-risk work the place a unsuitable reply prices nothing.
Join us and Barndoor AI for a reside session on separating the one step that wants AI reasoning from the remainder of the method, paired with an AI gateway that governs entry, spend, and visibility throughout your setting.
What you will take away:
- How to evolve past abilities. Why patching abilities to chase reliability has a ceiling, and what comes subsequent.
- Inference solely the place it is wanted. The steps that want judgment get AI. Everything downstream runs the identical approach, each time.
- Full visibility. Debugging an agent workflow ought to seem like debugging some other manufacturing system.
- Scope, then distribute. Set the guardrails, entry and instruments a workflow wants, then govern how groups and brokers run it.
Why you must attend:
- A course of that labored yesterday would possibly do one thing totally different at present. Most workflows are probabilistic finish to finish, not simply the place judgment is required.
- No constant entry management. Skills sit on one individual’s machine, and platforms that distribute a workflow govern it at the workflow stage, not the mannequin or MCP calls inside it.
- Cost and behavior are invisible from exterior the workflow. Nobody absolutely trusts it, a human retains checking, and AI stays parked on work that doesn’t matter a lot.
Your Speakers
Neil Mansilla VP of Platform, Barndoor AI
Neil runs platform technique at Barndoor AI, managing MCP server integrations and buyer rollouts. He beforehand led Developer Experience at Atlassian and labored as a software program engineer in e-commerce, actual property and healthcare.
Jay Parisi VP of Solutions Engineering, Barndoor AI
Jay architects the options that assist Barndoor clients govern and handle AI. He beforehand led enterprise options structure at Twilio, managed options structure at Coinbase, and labored in API administration at MuleSoft.
