“ai-agents”
Demystifying Graph Engineering in the Age of AI Agents
An overview of graph engineering for AI agents, covering harness, loop, and graph concepts, why it differs from knowledge graphs, and how fan-out, join, and router nodes power reliable agentic workflows.
AI-Driven Full-Stack Architecture: Token Economics of Building with Rust and Next.js
Explore the token economics of building full-stack apps with Rust and Next.js under AI-assisted workflows. Learn codebase tokenization patterns, agent loop costs, and optimization techniques.
Context-Driven Development: Managing AI Agents, System Architecture, and CI/CD with INTENT.md, DESIGN.md, and AGENTS.md
Learn how to enforce structured context for AI coding agents and CI/CD pipelines using INTENT.md, DESIGN.md, and AGENTS.md with automated GitHub Actions guardrails.
From Autocomplete to Agents: The Shift That Changes Everything
The AI that helps you write code has evolved. Understanding the fundamental difference between tools that suggest and tools that act.
Multi-Agent and Role-Based Work: When One Agent Isn't Enough
Learn when and how to scale from single agent to multi-agent systems with role-based development, structured handoffs, and adversarial review.
Prompting for Reliable Agents: The Four-Part Specification
Master the art of prompting for agentic coding with the four-part specification structure: Goal, Context, Constraints, and Done-When.
Supervising Agents Like a Team: The Contractor Model
Apply engineering management discipline to agentic work with the Contractor Model: scope, equip, run, review, gate on CI, and audit afterward.
Give the Agent a Check to Run: The Verification-First Approach
Design workflows where verification is part of the control loop from the start, enabling autonomous agent sessions you can walk away from.
Agentic Workflows with the Model Context Protocol (MCP)
A single prompt is not a workflow. Learn how to build multi-step agentic workflows on top of the Model Context Protocol: planning loops, tool chaining, human-in-the-loop gates, and orchestration patterns that survive production.
Beyond Gemini 3: Preparing for the Age of Fully Autonomous Digital Entities
Gemini 3 was the inflection, not the destination. Prepare your architecture, governance, and mindset for fully autonomous digital entities that run on their own.
Voice-Based Agentic OS: Building the Future with Flutter and Gemini 3
Your next operating system interface is speech. Build a voice-first agentic OS with Flutter and Gemini 3 — from wake word to autonomous task execution.
The "Ethics-by-Design" Framework: Embedding Values into Gemini 3 Agents
Don't bolt ethics on after the fact. Apply an Ethics-by-Design framework that embeds values, safety constraints, and audit trails directly into the Gemini 3 agent lifecycle.
Gemini 3 in Supply Chain: Predictive Reasoning for Global Logistics
Reactive supply chains are dead. Use Gemini 3 to reason over multimodal logistics signals and predict disruptions before they become port-wide delays.
The "Human-Agent Handover" Pattern: Seamless UX for Gemini 3 Support Bots
The best agents know when to stop. Design a seamless Human-Agent Handover that preserves context, escalates gracefully, and earns trust for Gemini 3 support bots.
Gemini 3 in FinTech: Autonomous Trading Bots with Real-time Risk Reasoning
Build an autonomous trading bot where Gemini 3 reasons about market risk in real time — with hard circuit breakers that keep the model from ever firing a losing order.
The Agent Auditor: Observability and Compliance for Gemini 3 Workflows
When agents act autonomously, who audits their decisions? Build an Agent Auditor that records, replays, and validates every Gemini 3 workflow step for observability and compliance.
Top AI Agent Frameworks for 2026: A Practical Comparison
A practical, evidence-based comparison of the leading AI agent frameworks in 2026 — LangGraph, CrewAI, OpenAI Agents SDK, AutoGen/AG2, Google ADK, and more — with guidance on how to choose.
Q2 2026 Retrospective: How the Gemini 3 Revolution Reshaped the Agentic AI Landscape
A developer-focused retrospective of the second quarter of 2026: Gemini 3.1 Pro maturity, Gemini 3.5 Flash GA, Deep Research Max, Managed Agents, Gemini Omni, and the agentic shift that defined Q2.
Observability for Agents: Tracing Tool Calls and Reasoning
An agent is a loop, not a single API call. Instrument it with OpenTelemetry AGENT/LLM/TOOL spans and a shared trace_id so you can attribute bad reasoning, slow dependencies, and tool failures.
The Self-Improving Codebase: Gemini 3 Agents that Refactor Based on Performance Logs
Self-improving agents edit their own harness based on mined execution traces. Learn the mine-propose-validate loop with Gemini 3 and strict regression gates.
Collaborative Coding: Leading a Team of Autonomous Gemini 3 Junior Developers
The junior-developer rung has been digitized. Learn how to orchestrate, review, and govern a team of autonomous Gemini 3 coding agents in 2026.
The "Audit Log" Pattern: Forensic Analysis of Gemini 3 Reasoning Chains
Debugging agents means auditing reasoning, not stack traces. Build a tamper-evident audit log of Gemini 3 reasoning chains for forensics and compliance.
The Sub-Agent Pattern: Delegating to Specialized Gemini 3 Workers
Single-LLM workflows suffer context saturation. Learn the sub-agent pattern: coordinator plus specialized Gemini 3 workers with scoped context, routing, and model tiering.
Attested Computations in Open Knowledge Format (OKF): Ensuring Verifiable AI Knowledge
Explore Attested Computations in OKF v0.2, bridging definitions and runnable queries so consumers can mechanically verify AI-generated insights.
Open Knowledge Format (OKF) v0.2: A Human- and Agent-Friendly Guide
Learn about Open Knowledge Format (OKF) v0.2, an open, human- and agent-friendly format for representing knowledge, provenance, trust, lifecycle, and attestation in AI systems.
Automated CI/CD Regression Evaluation for ADK Agents
Implement an automated Python quality gate for ADK agents in CI/CD pipelines to prevent trajectory regressions and enforce pass-rate thresholds before deployment.
Building a Dispatcher Agent with Specialized Subagents
Implement dynamic triage and intent-based routing using Google ADK dispatcher agents and specialized subagents for billing, tech support, and beyond.
Building an Eval Test Suite for ADK Agents
Move from vibes-based testing to structured evaluation for ADK agents, tracking tool call accuracy, trajectory efficiency, and LLM-as-a-judge quality scoring.
Building Custom Async Function Tools in Google ADK
Learn how to build non-blocking asynchronous function tools in Google ADK to handle database queries, REST APIs, and external microservices with high performance.
Building an Approval & Refund Graph Workflow with ADK 2.0
Step-by-step tutorial for constructing an auditable, graph-based refund processing workflow using ADK 2.0 and Python conditional routing.
Connecting MCP Tool Servers to ADK Agents
Connect Google ADK agents to Model Context Protocol (MCP) tool servers to seamlessly access pre-built integrations like SQLite, GitHub, Slack, and Google Drive without writing custom wrappers.
Cross-Language Agent Communication via A2A Protocol
Connect Python ADK agents with remote microservices and workers written in Go or Java using the Agent-to-Agent (A2A) HTTP standard protocol.
End-to-End OpenTelemetry Tracing for ADK Agents
Instrument Google ADK agent harnesses with OpenTelemetry to capture full-trajectory execution traces, tool latencies, and token metrics.
Getting Started with ADK 2.0 Graph Workflows
Explore ADK 2.0 Graph Workflows to combine LLM reasoning with deterministic Python routing for complex, auditable enterprise AI agent architectures.
Implementing Human-in-the-Loop Approval Gates in ADK
Safeguard high-value actions in Google ADK using Human-in-the-Loop (HITL) interrupt gates and before-tool callbacks.
Monitoring ADK Agent Fleets with BigQuery Analytics
Learn how to stream structured telemetry from your Google ADK agent fleets into BigQuery for SQL-based cost tracking, latency analysis, and quality evaluation.
Parallel Code Review & Security Audit with ADK
Build an automated code review harness combining ParallelAgent auditors for security and performance with a SequentialAgent synthesizer in Google ADK.
Safety Guardrails & Token Budgeting for ADK Agents
Implement multi-layered safety guardrails, PII redaction, token budgets, and error-rate circuit breakers in Google ADK agent harnesses.
Sequential & Parallel Agent Pipelines in Google ADK
Master multi-agent orchestration primitives in Google ADK using SequentialAgent for ordered pipelines and ParallelAgent for concurrent execution.
Session Rewind & Error Recovery for ADK Agents
Learn how to use session rewind in Google ADK to roll back conversation context upon tool failures, prompt injections, or policy violations without losing session state.
Stateful Multi-Turn Conversations with ADK's DatabaseSessionService
Build production-grade, stateful AI agents in Google ADK using DatabaseSessionService to persist shopping cart state, user preferences, and history across process restarts.
What Is an Agent Harness? Core vs. Scaffolding in Production AI Systems
Understand the essential architectural difference between an AI agent core and its production agent harness, and why frameworks like Google ADK are critical for building reliable agentic applications.
Your First ADK Agent Harness: Hello World with agents-cli
Build a complete, runnable agent harness using Google ADK, featuring custom function tools, session callbacks, and the Runner abstraction.
Anatomy of an Agentic Loop: The ORAE Cycle
Explore the fundamental Observe-Reason-Act-Evaluate cycle that powers modern autonomous AI agents, state persistence, and termination strategies.
Build a Custom Autonomous Coding Agent from Scratch
Build a production-ready autonomous coding agent loop in Python implementing the ORAE cycle, tool registries, safety gates, and budget guards.
Context Window Engineering for Long-Running Loops
Master context drift, context rot, memory compaction, Git worktree isolation, and token budgeting for reliable long-running AI agent loops.
Maker-Checker & Multi-Agent Orchestration Patterns
Learn advanced multi-agent patterns including Maker/Checker separation, fan-out/fan-in sub-agent orchestration, background scheduled loops, and autonomous goal-seeking systems.
An Introduction to Deep Agents: Building Multi-Step Autonomous LLM Systems
Discover how Deep Agents simplifies building LLM applications with built-in planning, sandboxed file systems, subagent spawning, and persistent memory.
The Rise of Cognitive Operating Systems: AI Agent Technology Trends and Enterprise Architecture in 2026
A comprehensive technical report on architectural breakthroughs, multi-agent systems, and enterprise deployment of AI agents in 2026.
The "Tool-Discovery" Protocol: How Gemini 3 Agents Find and Map New Capabilities
Build truly autonomous, self-expanding AI applications using the Tool-Discovery Protocol. Learn how Gemini 3 agents dynamically browse, map, and bind new API capabilities on the fly.
Telegram AI Bot: Building Modular Agents with ADK-Rust and Teloxide
Discover how to build a modular, extensible AI-powered Telegram bot using the ADK-Rust framework and Teloxide, featuring persistent sessions, tool modularity, and a modern TUI.
Mastering the Agents CLI: Your Gateway to Enterprise-Grade AI Agents
Learn how to build, evaluate, and deploy enterprise-ready AI agents using the Google Agents CLI (ADK).
Mastering the adk-cli: A Powerful Launcher for Your Rust AI Agents
Explore the features and capabilities of adk-cli, the essential command-line launcher for building, serving, and deploying Rust-based AI agents with ease.
The A2A Economy: Integrating Crypto Payments with Gemini 3 Agent Services
How to build autonomous agents that can pay each other for services using cryptocurrency in the Gemini 3 ecosystem.
Agentic RAG: Leveraging Gemini 3's Reasoning for Zero-Index Data Retrieval
How to move beyond traditional vector-based RAG using the reasoning capabilities of Gemini 3 for zero-index data retrieval.
Autonomous DevOps: Setting up a Self-Healing CI/CD with Gemini 3 Agents
How to leverage the reasoning power of Gemini 3 to create a truly autonomous, self-healing CI/CD pipeline.
Building "Digital Twin" Agents: Synchronizing App State with Gemini 3 Multimodal Streams
How to create AI agents that act as "Digital Twins" of your application, synchronized in real-time via Gemini 3 multimodal streams.
Building the Agentic Mesh: Mastering A2A Communication with adk-rust
Learn how to build a multi-agent system where specialized AI agents collaborate through the Agent-to-Agent (A2A) protocol using adk-rust.
Gemini 3: A Deep Dive into the 10M+ Token Context Window and Infinite Memory
Exploring the revolutionary 10 million token context window of Gemini 3 and how it enables infinite memory for AI agents.
PydanticAI vs. Gemini 3 Native Tools: Choosing the Right Framework
A comprehensive comparison between PydanticAI and the native agentic tools of Gemini 3 for building high-performance AI agents.
Beyond Code-First: Mastering Spec-Driven Development with SpecKit
An introduction to Spec-Driven Development and the Spec Kit toolkit.
RustFS MCP Server: High-Performance S3 Storage for AI Agents
Discover RustFS MCP Server, a high-performance tool providing AI/LLM assistants with standardized, secure access to S3-compatible object storage operations.
Mastering Agent Skills with adk-skill in Rust
Learn how to parse, index, match, and inject AgentSkills dynamically into your ADK-Rust applications using the adk-skill crate.
The ADK-Rust Tool Ecosystem: Empowering Your AI Agents
Discover the extensive and extensible tool ecosystem in ADK-Rust, from custom functions and browser automation to dynamic UI generation and MCP support.
FunctionTools vs. McpTools in ADK-Rust: Which Should You Use?
Understand the differences between custom Rust FunctionTools and standardized MCP tools for extending your AI agents.
Choosing the Right Multi-Agent Pattern: Balancing Complexity and Cost
Explore the primary multi-agent patterns—from single agents to sophisticated coordinators—and learn how to choose the right one for your AI project based on task complexity and budget.
Automate Your Pull Request Reviews with a Custom AI Agent
Build a custom AI agent to automatically review pull requests, check for code quality, and suggest improvements.
vdesk: Visualizing AI Agent Activities in a Retro 16-bit Office
Explore vdesk, an experimental project that brings AI agent internal states to life in a nostalgic 16-bit virtual office environment.
Stop Paying Thousands! Build Your Own Local AI Agent: The Secret Agencies Won’t Tell You
Discover how to build powerful local AI agents on your own hardware, saving money and keeping your data private.
Designing Agent Skills for DevOps and Platform Teams
A practical design approach for Agent Skills in DevOps contexts, focusing on reducing operational errors, enforcing standardized procedures, and maintaining strong security controls.
Designing Agent Skills for Enterprise Environments
A comprehensive guide on balancing operational efficiency, standardized architecture, and security when designing AI Agent Skills for organizations.
Managing Agent Data Access and Permissions in the Enterprise
A comprehensive guide to building secure and capable AI agents using Defense in Depth, Sandboxing, and Organizational Governance.
Integrating vLLM with Google ADK: A High-Performance Local LLM Guide
Learn how to leverage vLLM to host high-performance local LLMs and integrate them seamlessly with Google ADK using LiteLLM.
Semantic Layer: The Critical Bridge Between AI Agents and Enterprise Data
Exploring how a Semantic Layer acts as an architectural bridge, allowing AI agents to interact with enterprise data safely, accurately, and with business awareness.
Multi-Agent Technical Book Publishing System: Automating the Lifecycle with Google ADK
Explore a production-grade implementation for automating the lifecycle of a technical book using the Google Agent Developer Kit (ADK), featuring a layered cognitive approach with specialized agents.
Understanding Parallel Agents in Google ADK
Learn how Parallel Agents in Google ADK leverage concurrency to speed up complex workflows and manage independent sub-agents efficiently.
The Rise of Digital Coworkers: Navigating the Era of Enterprise AI Agents
Exploring how AI Agents are evolving from chatbots into digital coworkers that drive organizational efficiency through planning and execution.
AI Agents: The New Era of Digital Colleagues
Exploring the shift from conversational chatbots to goal-oriented AI agents and their role as digital colleagues in the modern workplace.
Introduction to Agent-to-Agent (A2A) Protocol in Google ADK
Explore the Agent2Agent (A2A) Protocol in Google ADK, understanding when to use remote agents over local sub-agents to build scalable, multi-agent systems.
Exploring the Ecosystem of Google ADK Integrations
A comprehensive overview of the tools and integrations available in the Google Agent Development Kit (ADK) to supercharge your AI agents.
Creating Effective File-Based Skills
A comprehensive guide on creating file-based skills for AI agents, covering the structure of SKILL.md and best practices for writing custom scripts.
Loop Agent in Google ADK
An in-depth look at the Loop Agent in Google ADK, a workflow agent that executes sub-agents iteratively for tasks requiring repetition and refinement.
Understanding Agent Skills
Discover how Agent Skills provide a lightweight, open format to extend AI agent capabilities with specialized workflows.
Enabling Agent-to-Agent (A2A) Support in PydanticAI
Learn how to expose your PydanticAI agents as Agent2Agent (A2A) protocol compliant servers for seamless interoperability.
Mastering the Web UI in PydanticAI: Local Debugging Made Easy
Learn how to quickly set up and use the built-in Web Chat UI for local development and debugging with PydanticAI agents.
Integrate FastMCP with FastAPI Applications
Discover how to effortlessly generate Model Context Protocol (MCP) servers from existing FastAPI applications or mount MCP endpoints within them using FastMCP.
Unlocking Complexity: A Guide to Multi-Agent Systems
Explore the power of Multi-Agent Systems (MAS) and how the Agent Development Kit (ADK) enables the creation of sophisticated, modular, and reusable AI applications.