Uploaded September 2026 | Updated September 2026, 3 weeks ago
Welcome to the Agentic AI Explained series. This is video 2 of 10, covering the key building blocks behind modern AI agent systems.
This video covers contextual policies in Omnigent, the open source meta-harness for AI agents. An Omnigent policy tracks session state, meaning what the agent has already read, done, and spent in the current session, and uses that state to decide whether the next action should proceed.
đź”— Learn more in the blog: databricks.com/blog/contextual-policies-omnigent-using-session-state-better-govern-ai-agents
đź”— GitHub repo for this series: github.com/viktoriasemaan/agentic-ai-explained-labs
The demo covers 2 of the built-in policies:
▪️ Risk score: each action adds to a running session score. While the score stays low the agent works uninterrupted. Once it crosses the threshold, actions like sending an email return ASK and wait for a human to sign off.
▪️ Cost budget: the policy tracks session spend against a soft checkpoint and a hard limit. At the checkpoint it pauses and asks whether to continue. At the hard limit it blocks further calls to the expensive model so the session can continue on a cheaper one.
Because Omnigent is a meta-harness, the same policies apply to any agent it wraps, including Claude Code, Codex, and custom agents.
Welcome to the Agentic AI Explained series. This is video 2 of 10, covering the key building blocks behind modern AI agent systems.
This video covers contextual policies in Omnigent, the open source meta-harness for AI agents. An Omnigent policy tracks session state, meaning what the agent has already read, done, and spent in the current session, and uses that state to decide whether the next action should proceed.
đź”— Learn more in the blog: databricks.com/blog/contextual-policies-omnigent-using-session-state-better-govern-ai-agents
đź”— GitHub repo for this series: github.com/viktoriasemaan/agentic-ai-explained-labs
The demo covers 2 of the built-in policies:
▪️ Risk score: each action adds to a running session score. While the score stays low the agent works uninterrupted. Once it crosses the threshold, actions like sending an email return ASK and wait for a human to sign off.
▪️ Cost budget: the policy tracks session spend against a soft checkpoint and a hard limit. At the checkpoint it pauses and asks whether to continue. At the hard limit it blocks further calls to the expensive model so the session can continue on a cheaper one.
Because Omnigent is a meta-harness, the same policies apply to any agent it wraps, including Claude Code, Codex, and custom agents.
