How Agentic AI Systems Combine RAG, Sandboxed Execution, and BigQuery at Scale

·1 views

A technical architecture for enterprise AI agents has been outlined, combining retrieval-augmented generation (RAG), sandboxed code execution, and BigQuery integration using Google's Agent Development Kit (ADK). The system uses Cloud Firestore vector search with text-embedding-005 embeddings to ground agent responses in live operational data, reducing hallucinations and prompt token overhead. Isolated Cloud Run sandbox environments allow agents to dynamically write and execute Python scripts for analytics without exposing host infrastructure. A human-in-the-loop safety mechanism requires explicit user approval before the agent pushes any changes to production systems such as Google Sheets. The architecture is designed to give enterprise AI agents scalable, standardized access to large datasets while maintaining security and operational oversight.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

SleepSwitch Keeps Macs Awake During Long AI Agent Runs, Lid Closed

A developer has released SleepSwitch, a free macOS menu bar utility that prevents a Mac from sleeping even when the lid is closed. The tool was created after repeated frustration with AI coding agents — such as Claude Code and Codex — failing mid-run because macOS treated the idle-looking machine as unused and put it to sleep. Standard workarounds like the caffeinate command do not block lid-close sleep, which requires a separate system-level setting normally locked behind root access. SleepSwitch handles both sleep layers with a single click by writing a limited sudoers rule at install time, avoiding repeated password prompts. The app also includes a battery guard and clears all sleep-blocking settings automatically when it quits to prevent the laptop from running hot unattended.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developers Can Detect Silent Filter Bugs by Asking Two Questions With Different Answers

A developer discovered a simple debugging technique after encountering silent filter failures twice in one session, where tools returned plausible but incorrect results without any error. The method involves posing two questions to a tool whose correct answers cannot be identical, then comparing the outputs to detect whether filtering is actually working. In one case, a mailbox search script returned the same count of 51 messages for two different queries, revealing that no filter had been applied at all. In another instance, a process count query returned six results, but a follow-up question exposed that two were stale leftover processes from the previous day. The author notes that this two-call technique requires no deep understanding of the tool and costs only one extra query to catch bugs that would otherwise go unnoticed.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Brazilian IT Student Shares Journey From Hardware Repair to Programming

A technology enthusiast has documented their career path, beginning with hands-on experience in computer assembly, maintenance, and troubleshooting. Over time, their interest expanded into software development, leading them to pursue programming logic courses. They are currently enrolled in an Information Technology Management degree at Universidade Cidade de São Paulo (UNICID), where they have gained exposure to Python and SQL databases. The individual aims to continue building practical skills and exploring new areas within the technology field. This personal account, published on DEV Community, is intended as the first in a series of posts tracking their ongoing learning journey.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Why a Single AI Chat Thread Fails as a Research Workspace

Software developer Omid argues that using a single AI chat session — whether ChatGPT, Claude, or Gemini — is fundamentally ill-suited for sustained research work. Chat threads are linear, lack honest memory, and cannot hold contradictory ideas that evolve over time, making them unreliable for long-term knowledge building. As context windows fill and summaries drop key details, researchers risk losing track of what was observed, guessed, or simply generated by the model. He proposes maintaining a separate, file-based workspace — such as Markdown notes stored in a Git repository — where ideas can remain messy, contradictory, and duplicated without pressure for early organization. In his view, AI chat should be a tool pointed at a knowledge base, not the knowledge base itself.

0 comments Read more at DEV Community