# LoopSmith: Closed-Loop AI Engineering for Self-Correcting Pipelines on Antigravity 2.0

DevFeed: [LoopSmith: Closed-Loop AI Engineering for Self-Correcting Pipelines on Antigravity 2.0](<https://devfeed.tech/articles/loopsmith-closed-loop-ai-engineering-autonomous-goal-execution-for-self-correcting-pipelines-on-22855.md>)

Original publisher: [Read original article](<https://medium.com/google-developer-experts/loopsmith-closed-loop-ai-engineering-autonomous-goal-execution-for-self-correcting-pipelines-on-22c915b0564b?source=rss----a67bd6fa7d58---4>)

Author: Esther Irawati Setiawan

Published: 2026-07-29T09:40:30Z

Content type: tutorial

Language: en

Sources: [Google Developer Experts - Medium](<https://devfeed.tech/sources/google-developer-experts-medium.md>)

Topics: [AI Engineering](<https://devfeed.tech/topics/ai-engineering.md>), [google-antigravity](<https://devfeed.tech/topics/google-antigravity.md>), [Agent Harness](<https://devfeed.tech/topics/agent-harness.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>), [Command-line interface](<https://devfeed.tech/topics/cli.md>), [Code](<https://devfeed.tech/topics/code.md>)

Tags: [agent-harness](<https://devfeed.tech/tags/agent-harness.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-engineering](<https://devfeed.tech/tags/ai-engineering.md>), [antigravity](<https://devfeed.tech/tags/antigravity.md>), [cli](<https://devfeed.tech/tags/cli.md>), [google-antigravity](<https://devfeed.tech/tags/google-antigravity.md>), [pipelines](<https://devfeed.tech/tags/pipelines.md>), [sdk](<https://devfeed.tech/tags/sdk.md>), [verification](<https://devfeed.tech/tags/verification.md>)

## AI overview

A guide to using Antigravity 2.0 to build closed-loop AI engineering workflows. It presents a state-machine pattern in which an agent writes, runs, and fixes code against a defined objective until the output is verified, while noting that the SDK is pre-v1.0 and its documented interfaces may change.

## Source excerpt

LoopSmith: Closed-Loop AI Engineering -- Autonomous /goal Execution for Self-Correcting Pipelines on Antigravity 2.0Stop prompting the agent turn by turn. Hand it an objective, a bar to clear, and let the state machine write, run, and fix its own code until the output is verified.This guide targets Antigravity 2.0 -- the four-surface release (desktop app, agy CLI, google-antigravity SDK, and enterprise cloud) that shares one agent harness. The SDK is pre-v1.0; symbol names and CLI flags below reflect the documented API as of mid-2026. Treat the patterns as stable and re-check exact signatures against the current docs before you ship.Table of contents The problem with the on-demand agent The architectural shift: closed-loop engineering as a state machine Step 1 -- Initialize the project and the state machine (agy) Step 2 -- Define the objective and trigger /goal execution mode (SDK) Step 3 -- Implement the self-correcting loop Step 4 -- Verification and state finalization Conclusion 1. The problem with the on-demand agent Most "AI engineering" today is still conversational. You prompt; the model answers. You notice the answer is wrong; you prompt again. You paste a traceback; it apologizes and tries once more. The intelligence is real -- but you are the control loop. You are the thing that runs the code, reads the error, decides whether the output is good enough, and feeds the next instruction back in. Take the human out of that seat and the whole system stops. That's fine for a chat window. It falls apart the moment you want an agent to produce a deliverable -- a cleaned dataset, a reconciled financial report, a migration that actually compiles. Real analytical work is iterative and self-referential: you write a script, it crashes on a currency string, you fix the parse, it runs but the totals don't reconcile, you fix the aggregation, and only then is the output trustworthy. Every one of those arrows is a decision. An on-demand agent makes you supply all of them. There are