AI Workflows in Agno: Building Deterministic Agents

Enterprise data is full of sensitive information: different teams, different access rights, different rules. When you ask an AI agent a simple question and get "access denied," it's not a permissions bug. It's a design problem.Pascal has been exploring how to tackle this using Agno, an agent framework built around deterministic workflows. Instead of letting a single agent roam freely across your data, Agno lets you build specialized agents, each with its own access rules and instructions. Workflows orchestrate these agents with guardrails that keep humans in the loop when it matters.In this episode, Pascal Knapen, CTO at Dataminded, demos the full flow: from a natural language question, through an access check, to a verified answer. We explore how skills differ from workflows, how Agno handles dynamic agent creation and deployment, and how LLM-based evaluations act as a quality judge for agent responses.Additional Resources:Demo code: https://github.com/datamindedbe/demo-technology-exploration/tree/main/demos/agno-workflowsCreators & Guests Pascal Knapen - Guest Jonny Daenen - Host ---Click here to watch a video of this episode. Full playlist: https://www.youtube.com/playlist?list=PLJ_da7qdfL80rA7byzC_CmyrfJWjcCTnb(00:00) - Intro: AI agents and enterprise data (01:50) - Two ways to give AI access to your data (03:23) - Skills vs Workflows (04:45) - Demo: AI with controlled data access (07:45) - The workflow is deterministic - the agents aren't (09:13) - Demo: evaluations & reliability (11:00) - Code walkthrough with Agno (17:45) - Why Agno? An honest take (19:46) - Identity & exposing as an API (20:24) - Takeaways ---Data & AI: Technology Explorations is a biweekly show from Dataminded. Each episode a Dataminded engineer demos a tool or technique worth knowing about -- working code, honest takes, no hype.Music by Aleksandr Karabanov from Pixabay