All news with #agent development kit tag

Build Your First AI Agent Workforce with Google's ADK

🤖 Google’s open-source Agent Development Kit (ADK) simplifies creating autonomous AI agents that use LLMs such as Gemini as their reasoning core. The post presents three hands-on codelabs that guide developers through building a personal assistant agent, adding custom and third-party tools, and orchestrating multi-agent workflows. Each lab demonstrates practical patterns—scaffolding an agent, integrating tools like Google Search and LangChain components, and using Workflow Agents and session state to pass information—so teams can progress from experiment to production-ready agent systems.

Vertex AI Agent Builder: Build, Scale, Govern Agents

🚀 Vertex AI Agent Builder is Google Cloud's integrated platform to build, scale, and govern production AI agents. The update expands the Agent Development Kit (ADK) and Agent Engine with configurable context layers to reduce token usage, an adaptable plugins framework, and new language SDK support including Go. Production features include observability, evaluation tools, simplified deployment via the ADK CLI, and strengthened governance with native agent identities and Model Armor protections.

Agent Factory Recap: Evaluating Agents, Tooling, and MAS

📡 This recap of the Agent Factory podcast episode, hosted by Annie Wang with guest Ivan Nardini, explains how to evaluate autonomous agents using a practical, full-stack approach. It outlines what to measure — final outcomes, chain-of-thought, tool use, and memory — and contrasts measurement techniques: ground truth, LLM-as-a-judge, and human review. The post demonstrates a 5-step debugging loop using the Agent Development Kit (ADK) and describes how to scale evaluation to production with Vertex AI.

Startup Technical Guide: Building Production AI Agents

🤖 Google Cloud published the Startup technical guide: AI agents, a practical, operations-driven roadmap to design, build, and operate agentic systems for startups. The guide outlines three paths — build with the open-source Agent Development Kit (ADK), design no-code agents in Agentspace, or adopt managed and partner agents via Vertex AI and the Agent Garden marketplace. It details four development steps (identity, prime directive, tools, lifecycle), highlights operational rigor (AgentOps), and promotes interoperability through standards such as MCP and A2A, all aimed at safe production deployment.

AI Forecasting and Conversational Analytics in BigQuery

🔎 Google added two BigQuery tools—ask_data_insights and BigQuery Forecast—to the MCP Toolbox and the Agent Development Kit (ADK) to enable conversational analytics and time-series predictions for agents. ask_data_insights uses the Conversational Analytics API to interpret plain-English questions, generate and run queries, and return summarised answers with a step‑by‑step log for transparency. BigQuery Forecast leverages BigQuery ML’s TimesFM model via AI.FORECAST so agents can run forecasting jobs directly inside BigQuery without separate ML infrastructure.