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Omnigent

Omnigent is Databricks' open-source AI agent framework, notable for its built-in contextual policy engine that evaluates every tool call against multiple policy layers before allowing execution.

Architecture

The framework runs a single policy engine per session that evaluates tool calls against a stack of contextual policies. Each policy returns one of three verdicts:

  • ALLOW — action proceeds.
  • ASK — action is paused for human approval.
  • DENY — action is blocked.

Policies compose with deny-wins semantics: if any single policy denies an action, it is blocked regardless of what other policies say. This makes the policy set monotonically restrictive — adding a new permissive policy cannot override an existing denial.

Built-in policy types

  • Intent-based authorization — binds a session to a declared purpose; any tool call outside that purpose is denied or gated (patterns/intent-based-authorization).
  • Session-risk scoring — behavioral anomaly detection that tracks drift from expected session patterns (described in companion blog "Blocking Slow-Burn Attacks").
  • PII blocking — prevents exposure of sensitive data in agent outputs.
  • Custom rules — user-defined policies in plain language, compiled to enforceable rules.

Key design decisions

  1. Intent is immutable at runtime. For autonomous agents, intent is pinned in the agent spec. For interactive agents, intent is set at session start with human approval. The running agent has no tool to edit, remove, or disable policies.

  2. Adding policies requires human approval. Even if an agent has a "add policy" tool, a built-in meta-rule gates new policy activation on explicit user consent.

  3. Deny-wins composition prevents policy escalation — a new permissive rule cannot lift an existing block (patterns/deny-wins-policy-composition).

Status

Open source, in alpha as of July 2026. Documentation at omnigent.ai/docs.

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