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Bring state-of-the-art agentic skills to the edge with Gemma 4Supporting Google Account username change in your appDeveloper’s Guide to Building ADK Agents with SkillsADK Go 1.0 Arrives!Boost Training Goodput: How Continuous Checkpointing Optimizes Reliability in Orbax and Ma...Announcing ADK for Java 1.0.0: Building the Future of AI Agents in JavaClosing the knowledge gap with agent skillsJump to play: Building with Gemini & MediaPipeBuild a smart financial assistant with LlamaParse and Gemini 3.1Developer’s Guide to AI Agent ProtocolsAnnouncing the Colab MCP Server: Connect Any AI Agent to Google ColabPlan mode is now available in Gemini CLIIntroducing Finish Changes and Outlines, now available in Gemini Code Assist extensions on...Unleash Your Development Superpowers: Refining the Core Coding ExperienceIntroducing Wednesday Build HourWhat's new in TensorFlow 2.21You can't stream the energy: A developer's guide to Google Cloud Next '26 in VegasHow we built the Google I/O 2026 Save the Date experienceSupercharge your AI agents: The New ADK Integrations EcosystemOn-Device Function Calling in Google AI Edge GalleryTorchTPU: Running PyTorch Natively on TPUs at Google ScaleGet ready for Google I/O: Livestream schedule revealedNew enhancements for merchant initiated transactions with the Google Pay APIBuild Better AI Agents: 5 Developer Tips from the Agent Bake-OffBuilding with Gemini Embedding 2: Agentic multimodal RAG and beyondProduction-Ready AI Agents: 5 Lessons from Refactoring a MonolithSubagents have arrived in Gemini CLIMaxText Expands Post-Training Capabilities: Introducing SFT and RL on Single-Host TPUsAgents CLI in Agent Platform: create to production in one CLIA2UI v0.9: The New Standard for Portable, Framework-Agnostic Generative UIBring state-of-the-art agentic skills to the edge with Gemma 4Supporting Google Account username change in your appDeveloper’s Guide to Building ADK Agents with SkillsADK Go 1.0 Arrives!Boost Training Goodput: How Continuous Checkpointing Optimizes Reliability in Orbax and Ma...Announcing ADK for Java 1.0.0: Building the Future of AI Agents in JavaClosing the knowledge gap with agent skillsJump to play: Building with Gemini & MediaPipeBuild a smart financial assistant with LlamaParse and Gemini 3.1Developer’s Guide to AI Agent ProtocolsAnnouncing the Colab MCP Server: Connect Any AI Agent to Google ColabPlan mode is now available in Gemini CLIIntroducing Finish Changes and Outlines, now available in Gemini Code Assist extensions on...Unleash Your Development Superpowers: Refining the Core Coding ExperienceIntroducing Wednesday Build HourWhat's new in TensorFlow 2.21You can't stream the energy: A developer's guide to Google Cloud Next '26 in VegasHow we built the Google I/O 2026 Save the Date experienceSupercharge your AI agents: The New ADK Integrations EcosystemOn-Device Function Calling in Google AI Edge GalleryTorchTPU: Running PyTorch Natively on TPUs at Google ScaleGet ready for Google I/O: Livestream schedule revealedNew enhancements for merchant initiated transactions with the Google Pay APIBuild Better AI Agents: 5 Developer Tips from the Agent Bake-OffBuilding with Gemini Embedding 2: Agentic multimodal RAG and beyondProduction-Ready AI Agents: 5 Lessons from Refactoring a MonolithSubagents have arrived in Gemini CLIMaxText Expands Post-Training Capabilities: Introducing SFT and RL on Single-Host TPUsAgents CLI in Agent Platform: create to production in one CLIA2UI v0.9: The New Standard for Portable, Framework-Agnostic Generative UI

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Bring state-of-the-art agentic skills to the edge with Gemma 4

Google DeepMind has launched Gemma 4, a family of state-of-the-art open models designed to enable multi-step planning and autonomous agentic workflows directly on-device. The release includes the Google AI Edge Gallery for experimenting with "Agent Skills" and the LiteRT-LM libra...

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ADK Go 1.0 Arrives!

The launch of Agent Development Kit (ADK) for Go 1.0 marks a significant shift from experimental AI scripts to productio...

▶ Tages-Digest — 08. Mai 2026 816 Artikel, 27 relevant
KI-Tagesüberblick 2026-05-08

### Highlights

1. Prozess statt Output zur Mensch-Maschine-Unterscheidung — [Anthropic, OpenAI und Google Forschung](https://arxiv.org/abs/2605.06524) zeigt: Die Art, wie KI-Systeme zu Ergebnissen gelangen, ist aussagekräftiger als nur die Outputs selbst. Dies könnte traditionelle Turing-Test-Ansätze grundlegend verändern.

2. Agentenverhalten messbar machen — [Neue Bewertungsrahmen für agentic Systeme](https://arxiv.org/abs/2605.05739) ermöglichen detaillierte Verhaltensanalyse statt nur aggregierter Kennzahlen. Besonders relevant für Finanzanwendungen und komplexe Decision-Making-Prozesse.

3. Token-Effizienz in agentic Workflows — [GitHub verbessert die Kosteneffizienz](https://github.blog/ai-and-ml/github-copilot/improving-token-efficiency-in-github-agentic-workflows/) von automatisierten Workflows erheblich, um das wachsende Kostenproblem bei CI-Jobs zu adressieren.

4. KI-Podcasts bei Spotify — [OpenClaw und Claude integrieren mit Spotify](https://www.theverge.com/entertainment/925916/save-to-spotify-ai-podcasts) über neue Command-Line-Tools für AI-Agenten.

5. Zuverlässigere Confidence-Estimation — [Neue Methode zur Black-Box-Konfidenzschätzung](https://arxiv.org/abs/2605.06308) reduziert rechnerische Kosten bei Chain-of-Thought-Reasoning durch Trajektorie-Analyse statt reiner Sample-Konsistenz.

### Forschung

- [ReFlect-System](https://arxiv.org/abs/2605.05737) — Neuer Rahmen für LLM-Reasoning, der Fehler in Multi-Stage-Tasks automatisch erkennt und Recovery-Strategien aktiviert.

- [MemReranker](https://arxiv.org/abs/2605.06132) — Reasoning-fähiges Reranking für Agent-Memory-Retrieval behebt das Problem semantisch relevanter, aber inhaltlich unbrauchbarer Suchergebnisse.

- [ZAYA1-8B Technical Report](https://arxiv.org/abs/2605.05365) — Mixture-of-Experts-Modell mit 8B Parametern (700M aktiv) konkurriert mit DeepSeek-R1 in Mathematik und Coding bei minimalerem Ressourcenverbrauch.

- [AgenticRAG](https://arxiv.org/abs/2605.05538) — Praktischer Ansatz für Agentic Retrieval in Enterprise-Wissensdatenbanken reduziert Abhängigkeit von festen Retrieval-Kandidatenmengen.

- [TACT: Activation Steering](https://arxiv.org/abs/2605.05980) — Adressiert "Agent Drift" durch Vermeidung von Overthinking und Overacting in Code-Agenten über lange Trajektorien.

- [Constraint Decay](https://arxiv.org/abs/2605.06445) — Zeigt Schwachstellen von LLM-Agenten bei strukturellen Anforderungen (Architektur, Datenbanken, ORM) in Backend-Codegenerierung.

- [RAG-Sicherheit unter Poisoning](https://arxiv.org/abs/2605.05632) — Multi-Agent-Debate und agentic Retrieval werden erstmals systematisch gegen Knowledge-Base-Vergiftung getestet.

- [PrefixGuard](https://arxiv.org/abs/2605.06455) — Framework für Echtzeit-Fehlerwarnung in LLM-Agent-Traces ohne teure Deployment-Zeit-Judgment.

### Tool-Releases

- [Claude Code v2.1.133](https://github.com/anthropics/claude-code/releases/tag/v2.1.133) — Neue `worktree.baseRef` Settings für flexiblere Git-Branching-Strategien in Agentic Workflows.

- [Claude Agent SDK TypeScript v0.2.133](https://github.com/anthropics/claude-agent-sdk-typescript/releases/tag/v0.2.133) — V2 Session API deprecated, Migration zu `query()`-API, Parität mit Claude Code erreicht.

### Sonstiges

- [SWE-Pruner](https://arxiv.org/abs/2601.16746) — Self-Adaptive Context Pruning für Coding Agents reduziert lange Interaktions-Kontexte und API-Kosten durch Task-spezifische Kompression statt generischer PPL-Metriken.

- [Flexible Agent Alignment](https://arxiv.org/abs/2508.15119) — Open-Universe-Assistance-Games-Framework für bessere Multi-Turn-Agenten, die sich an evolvierende User-Intents anpassen.
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