Click any tag below to further narrow down your results
Links
Google engineers demonstrate how AI systems are evolving from basic retrieval-augmented generation (RAG) to graph-based architectures that handle more complex reasoning and multimodal tasks. The 90-minute workshop walks through building production agent stacks with semantic graph retrieval and specialized agent orchestration.
- RAG is being replaced by graph-based approaches that organize context semantically rather than just retrieving relevant documents
- The production stack involves extracting graph context and orchestrating multiple specialized agents to handle different tasks
- This represents a concrete shift in how companies are building AI systems—from simple retrieval to structured knowledge representation
Omnigent adds a universal layer above existing agent harnesses—like Claude Code, Codex, and custom agents—to let you mix models, enforce stateful policies, and collaborate live in shared sessions. It provides one-line agent swaps, contextual security and cost controls, OS sandboxing, cloud execution, and real-time collaboration, all open-sourced under Apache 2.0.
- Databricks open-sourced Omnigent (Apache 2.0) as a meta-harness that sits above Claude Code, Codex, and custom agents, letting you swap models or harnesses with one line of code instead of rewriting logic.
- It enforces runtime policies rather than prompt-based guardrails—e.g., pausing a session after $100 in LLM spend or requiring human approval before a git push if new npm packages were installed.
- It adds real-time multi-user collaboration via shareable URLs, cross-platform clients (web, mobile, macOS), and cloud execution through providers like Modal, Daytona, Fly.io, or Railway.
- Agents are defined once in YAML and portable across harnesses, with an OS-level sandbox that intercepts network requests to control when secrets are injected.
+ omnigent
agent orchestration
+ policy enforcement
+ multi user collaboration
+ devtools
+ security
Paperclip is a platform that manages AI agents to streamline business operations. It allows users to set goals, hire agents, and monitor their performance from a centralized dashboard. Unlike traditional task managers, Paperclip integrates organizational structures and budget controls for efficient agent coordination.
- Paperclip orchestrates multiple AI agents as a unified company rather than just managing individual tasks, adding org charts, budget controls, and goal alignment on top.
- It runs locally via Node.js/pnpm with an embedded, self-managed PostgreSQL database, and can run multiple isolated companies from one deployment.
- Agents run on schedules or event triggers with built-in cost tracking to prevent budget overruns and maintain persistent state.
- The platform supports plugins, allowing extension beyond its core orchestration and governance features.