S2:E12

AI Workflows in Agno: Building Deterministic Agents

AI
22 min

Host

Jonny DaenenJonny Daenen

Guest

Pascal KnapenPascal Knapen

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.

Read transcript →

Chapters

  1. 00:00Intro: AI agents and enterprise data
  2. 01:50Two ways to give AI access to your data
  3. 03:23Skills vs Workflows
  4. 04:45Demo: AI with controlled data access
  5. 07:45The workflow is deterministic - the agents aren't
  6. 09:13Demo: evaluations & reliability
  7. 11:00Code walkthrough with Agno
  8. 17:45Why Agno? An honest take
  9. 19:46Identity & exposing as an API
  10. 20:24Takeaways

Tags

AgnoAI Agent FrameworkEnterprise Data AgentsDeterministic WorkflowsAgent Skills vs WorkflowsData Access ControlLLM EvaluationMulti-Agent SystemsPython AI FrameworkAgentic AIHuman-in-the-Loop AISecure AI Agents