philschmid.de - RSS feed

Frontier coding agents can compose shell workflows that replace entire catalogs of file tools.

Five commands, zero fluff: install OpenClaw 2.0, connect Gemini 3.8 Flash, and start chatting with live Google Search grounding from your terminal and web dashboard in under a minute.

Agents can already edit their tools, skills, and harness.

Use Gemini 3.7 Flash Computer Use with about 150 lines of Python to control an Android emulator.

Use Google Maps and Google Search in the same Gemini API call with Gemini 3.6 Flash, then add custom functions or MCP servers for actions like booking a table.

EvoCode-Bench tests coding agents across 227 sequential rounds in a persistent workspace.

Build Gemini Managed Agents that use the GitHub CLI without exposing your PAT.

Control an Android emulator using Gemini 3.5 Flash Computer Use.

A developer guide to the Gemini Interactions API, text generation, streaming, multi-turn chat, multimodal inputs, structured output, tool use, managed agents, and background execution from a single endpoint.

A single API call boots a sandbox, loads skills, and lets the model loop until the task is done.

Build, customize, and deploy production agents that reason, execute code, and manage files inside secure Linux sandboxes as a single API call.

Subagents solve context pollution, but how the main agent manages them matters more than whether they run in sync or async.

The Gemini Deep Research Agent autonomously plans, searches, and synthesizes long-horizon research tasks into detailed, cited reports.

MCP servers are not dead.

8 Tips for Writing Agent Skills.

Learn how to use Gemma 4 with the Gemini API and Google AI Studio.

Learn the unique ways how Kimi, Cursor, and Chroma train agentic models with RL.

Learn how to combine built-in tools and function calling in the Gemini Interactions API.

Learn how to use the Gemini Interactions API to build a personalized Japan travel brochure with Nano Banana 2.

Autoresearch lets an AI agent run hundreds of model training experiments overnight.

Learn how to systematically test and improve agent skills using deterministic checks and a real-world Gemini API example.

Learn what to include, what to skip, and how to structure your AGENTS.md for best results.

Most agent frameworks share the same hardcoded tool loop; what differs is how the model uses it.

What makes some AI agents feel like collaborators while others need constant babysitting?

Multimodal function calling allows tools to return images the model can process natively, similar to how you pass images in prompts.

Learn how to use the new Gemini Deep Research agent via the Interactions API to perform complex research tasks, generate images based on the findings, and translate the results.

The Agent Client Protocol (ACP) is an open standard abstracts the events and outputs of AI agents and provides a common interface for editors to interact with them.

The Interactions API is a unified interface for building with Gemini models and agents.

The Model Context Protocol (MCP) has exploded roughly 1 year ago, everyone rushed to build MCP servers.

Learn how to generate transparent PNG stickers using Nano Banana Pro and the Gemini Interactions API, featuring chromakey green background removal with HSV detection.

Learn how to build AI agents using the new Gemini Interactions API, featuring server-side state management and simplified tool orchestration.

Mcp-cli is a lightweight CLI that allows dynamic discovery of MCP, reducing token consumption while making tool interactions more efficient for AI coding agents.

In 2026, Agent Harnesses will become essential for building reliable AI systems that can handle complex, multi-day tasks.

8 Predictions for 2026, exploring the future of AI, personal agents, smart homes, and more.

Building on the foundations of Context Engineering, this post explores advanced strategies to manage context rot, multi-agent coordination, and action space optimization for AI agents.

Traditional software engineering is deterministic, while AI agents operate probabilistically.

A step-by-step practical guide on building AI agents using Gemini 3 Pro, covering tool integration, context management, and best practices for creating effective and reliable agents.

A comprehensive guide on best practices for prompting Gemini 3, focusing on clarity, structure, reasoning, and agentic tool use to maximize model performance across various domains.

Learn how to use the Gemini API File Search tool with JavaScript/TypeScript to build a Retrieval-Augmented Generation (RAG) system.

Learn how to deploy n8n on Google Cloud Run with PostgreSQL and create an AI Agent using Google Gemini 2.5.

An extensive compendium of over 50 benchmarks for evaluating AI agents, categorized into Function Calling and Tool Use, General Assistant and Reasoning, Coding and Software Engineering, and Computer Interaction.

An overview of the architectural shift from Shallow Agents (Agent 1.0) to Deep Agents (Agent 2.0) and how to build complex AI agents that can handle multi-step tasks over extended periods.

The rise of subagents is a trend in the AI community.

Learn how to use Gemini 2.5 Flash for product image generation.

Learn how to engineer long-term memory into stateless AI agents to overcome their biggest limitation and unlock true personalization.

A comprehensive cheatsheet on using Google's Gemini CLI, covering installation, authentication, configuration, and core commands.

Code Sandbox MCP is a simple, self-hosted code interpreter for your AI agents.

This guide shows you how to add long-term memory to your Gemini 2.5 chatbot using the Gemini API and Mem0.

Context Engineering is the new skill in AI.

Single vs. multi-agent?