How to build fast cheap AI agents like Jev?

TL;DR

Jev is a fast, free decision engine that routes inputs to specific tasks with confidence scores rather than producing text. It can classify dictation, map to tasks, and call functions in real time, enabling live to-do apps, data discovery, and multi step workflows.

Transcript

In some ways, managers are really at risk. This concept of like org design and role design, you can put those skills to use when crafting your [music] agents, which is why I am currently running, no joke, 40 Grock bots right now. >> Whenever you have constrained inputs that are interacting with apps, it's Jev is a [music] good thing to reach for. T... Read More

Key Insights

• Jev outputs decisions and scores, not free form text.
• Jev operates as a fast, free decision engine that can process large data sets quickly.
• The model uses multi stage classification to route inputs to the right task or function.
• Confidence scores help validate each step before taking action.
• Jev can handle dictation, check for dictation accuracy, and map to existing tasks.
• Jev is designed to minimize cost while maximizing throughput for data heavy tasks.
• Jev functions as a multi level app router, allowing deep navigation from a single input.
• The framework supports sequential calls where one Jev pass informs the next action for accuracy.

Questions & Answers

Q: What outputs does Jev provide and why is that useful?

Jev outputs structured decisions and confidence scores rather than unstructured text. This makes it a reliable decision engine that can drive downstream actions, APIs, and functions with clear signals. The structured outputs reduce ambiguity, improve automation reliability, and enable faster integration across complex workflows.

Q: How does Jev handle real time tasks like a to do list?

In a real time to do list demo Jev classifies incoming dictation, matches it to a task, and selects the appropriate function call to perform the action. It does this with several passes that check phrasing, category, and desired operation, all while dictation continues streaming.

Q: What makes Jev fast and cost effective according to the video?

Jev is described as fast and free, enabling instant function calls without long LLM latency. The speakers share experiences with processing hundreds of records at low cost, illustrating that large data sets can be handled without prohibitive spend, which expands the range of potential use cases.

Q: How does Jev handle data discovery and ROI?

Jev enables discovery over data by providing quick, structured insights rather than raw text, which opens opportunities and reveals high ROI tasks that might have been ignored with heavier models. This rapid exploration helps identify valuable data-driven workflows with minimal cost.

Q: What is the role of confidence scores in Jev?

Confidence scores are used to validate dictation accuracy, task matching, and the chosen function call. They guide the decision process to select the best possible action and provide a measurable metric for reliability of each step in the pipeline.

Q: How is Jev different from a traditional chatbot?

Jev is a decision engine rather than a chatbot. It converts unstructured input into structured actions, routing data to the right APIs and services. This shifts the focus from generating text to orchestrating tasks and ensuring correct execution across a system.

Q: Can Jev handle multi step workflows and why is that important?

Yes, Jev supports multi step classification and sequential calls so one pass may inform the next action. This allows complex workflows to be executed with escalating checks and refined routing, enabling sophisticated user journeys that adapt based on intermediate results.

Q: What are potential limitations of Jev mentioned in the video?

The video notes that Jev may fall short for tasks requiring full generative models and when simpler tools cannot capture necessary nuance. It emphasizes using Jev as a router and decision engine for many cases while acknowledging certain scenarios where a full LLM is still preferable.

Summary & Key Takeaways

• Jev provides a live, real time decision layer that outputs structured results instead of natural language text. It handles input classification, task matching and function calls on the fly, enabling interactive apps with streaming input.

• The system emphasizes speed and cost efficiency, allowing developers to explore data discovery and workflow automation with minimal budget and no long running inference costs.

• In product settings Jev acts as a router for multiple services, allowing a single input to drive complex, multi step user journeys across apps and APIs.