Posts by Eric Dong

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  • AUG. 17, 2026 / AI

    Build zero-trust AI agents with Google's Agent Development Kit

    Building autonomous AI agents that mutate production state requires moving beyond soft system prompts to a robust zero-trust architecture. To secure Google Agent Development Kit (ADK) workflows against prompt injections and malicious execution, developers must implement hardware-backed cryptographic signatures for database writes, kernel-level sandboxing with gVisor for dynamic code, and deterministic semantic gateways for I/O validation. By enforcing these hard security boundaries at the infrastructure level, you can safely deploy multi-tool AI agents without risking unauthorized data manipulation or server compromise.

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  • JUNE 22, 2026 / AI

    Build Cross-Language Multi-Agent Team with Google’s Agent Development Kit and A2A

    How a Python agent and a Go agent collaborate on contract compliance using the Agent2Agent protocolY...

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  • MAY 12, 2026 / AI

    Build Long-running AI agents that pause, resume, and never lose context with ADK

    How to transition from stateless chatbots to production-grade agents capable of managing long-running enterprise workflows, such as HR onboarding, that span days or weeks. It introduces the Agent Development Kit (ADK) and its architectural shifts, specifically using durable state machines and persistent session storage to ensure an agent never loses context during "idle time" or server restarts. By leveraging event-driven webhooks and multi-agent delegation, the tutorial demonstrates how to build resilient systems that "sleep" during pauses and wake up to resume complex tasks with high reasoning accuracy.

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  • JULY 16, 2025 / AI

    Unlock Gemini’s reasoning: A step-by-step guide to logprobs on Vertex AI

    The `logprobs` feature has been officially introduced in the Gemini API on Vertex AI, provides insight into the model's decision-making by showing probability scores for chosen and alternative tokens. This step-by-step guide will walk you through how to enable and interpret this feature and apply it to powerful use cases such as confident classification, dynamic autocomplete, and quantitative RAG evaluation.

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