GitHub - VIKAS9793/antigravity-continuity-engine: Designing a continuity-first system for agentic AI workflows under quota constraints in Google Antigravity IDE

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A system-level product case study exploring why agentic AI workflows fail under quota constraints—and how to design for continuity.

Status Focus Topic

Continuity Engine — A Product Case Study

Solving Workflow Interruption for Google Antigravity IDE

"Agent workflows should not stop due to quota limits. The system should preserve continuity by intelligently switching models and managing resources."

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Deliver a better experience by choosing the format that fits your schedule.

Option 1: Watch the Visual Gist (3 min)

Important

Prefer a visual breakdown? Watch the video summary to understand the product vision, the quota crisis, and the Continuity Engine solution in just 3 minutes.

Watch the Video Case Study

▶️ Watch on YouTube: Reimagining Continuity in Google Antigravity

Option 2: Read the Full Technical Study

Deep-dive into the raw research, failure mode analysis, and technical specifications below.

What This Demonstrates

Skill Evidence in This Case Study
Problem Discovery Identified from direct product usage; not a hypothetical
Data-Backed Validation Proxy metrics derived from community discourse, issue trackers, and forum telemetry
Systems-Level Thinking Root cause analysis across quota architecture, UX, model routing, and state management
Product Opportunity Framing 5 actionable product proposals with implementation notes and risk trade-offs
Honest Trade-off Reasoning Acknowledges compute costs, hallucination risks, and competitive pressures

Case Study Structure

The Core Problem in One Diagram

User initiates agentic task
        │
        ▼
Agent begins deep multi-file execution
        │
        ▼
   [~45 min mark]
Quota exhaustion hits mid-workflow
        │
        ├──► Hard stop. No warning.
        ├──► Context lost. Files left in broken state.
        ├──► 167-hour (7-day) lockout triggered.
        └──► User must manually restart from scratch.

Current TCR: 45–52%    QIR: 68–75%    UIR: 82–88%

Key Metrics at a Glance

Metric Value Implication
Task Completion Rate (TCR) 45–52% Most agentic workflows fail before completion
Quota Interruption Rate (QIR) 68–75% Majority of deep sessions end in forced termination
User Intervention Rate (UIR) 82–88% True autonomy is rarely sustained
Workaround Adoption Rate (WAR) 35–42% Users are building their own continuity systems

The Central Product Insight

Users are manually orchestrating model hierarchies, writing .antigravityignore files, and using third-party memory extensions — not because they want to, but because the platform does not provide a continuity layer.

The product gap is not a missing feature. It is a missing system layer.

Proposed Solution: The Continuity Engine

Five interlocking product capabilities:

  1. Intelligent Task-Based Model Routing — auto-assign models by task complexity
  2. Quota-Aware Predictive Execution — pre-flight cost estimation before execution
  3. Fiduciary Circuit Breakers — halt runaway agentic loops before quota drain
  4. Session State Continuity (Handoff) — serialize and resume agent state across boundaries
  5. Decoupled Quota Pools — isolate high-reasoning and high-velocity model buckets

→ Full specification: 06-continuity-engine-proposal.md

Research Sources

This case study draws from:

  • Google AI Developer Forum threads (Feb–Apr 2026)
  • Reddit communities: r/GoogleAntigravityIDE, r/google_antigravity, r/Bard
  • Developer blogs: antigravity.codes, augmentcode.com
  • GitHub issue trackers and community mastery handbooks
  • Direct platform usage and behavioral observation

Full citations are included within each document.

📄 See DISCLAIMER.md for the full independent research disclaimer.

Case Study Ownership

Author: VIKAS SAHANI
GitHub: @VIKAS9793
LinkedIn: Vikas Sahani
Email: vikassahani17@gmail.com

Repository: antigravity-continuity-engine