Autonomix AI

The autonomous multi-agent operational engine powering end-to-end business workflows, customer onboarding, and micro-enterprise growth on Google Cloud.

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Inspiration

Traditional business scaling relies on expanding human headcount to execute repetitive operational tasks—lead qualification, customized client intake, compliance tracking, and service drafting. For micro-entrepreneurs and small business owners, this operational friction creates a bottleneck that stifles growth and limits market reach.

With recent advances in autonomous agent systems, operations that once required whole engineering or operations departments can now be orchestrated by specialized AI agents. Autonomix AI was inspired by a simple question: What if a single founder could operate a high-throughput, enterprise-grade company where AI agents execute core day-to-day decisions in production?

What it does

Autonomix AI is an autonomous multi-agent operational engine designed to power end-to-end business workflows, customer onboarding, and service execution on Google Cloud.

Instead of acting as a simple point-and-click UI wrapper, Autonomix runs a self-coordinating team of 4 specialized AI agents that process customer requests, make operational decisions, generate structured deliverables, audit compliance, and trigger transactional billing without human intervention:

Sourcing & Ingestion Agent: Scans incoming payloads, customer requirements, and external market streams, transforming unstructured inputs into validated JSON schemas.

Strategy & Reasoning Agent: Evaluates constraints, checks historical database contexts, and calculates execution pathways.

Execution & Deliverable Agent: Drafts custom high-value assets (such as tailored pitch proposals, client onboarding packages, or service reports).

Compliance & Audit Agent: Verifies generated deliverables against strict policy guidelines, character bounds, and formatting criteria before dispatching outputs and triggering the Stripe billing pipeline.

How we built it

Autonomix AI was engineered ground-up around the Google Cloud ecosystem:

Model Orchestration: Powered by Gemini 2.5 Flash for high-speed schema extraction and compliance auditing, and Gemini 2.5 Pro via Vertex AI for deep reasoning and narrative generation.

Backend Infrastructure: Built with Python and deployed as a microservice on Google Cloud Run for auto-scaling serverless execution.

Data & Log Persistence: Google Cloud Firestore stores real-time execution states, customer parameters, and structured agent outputs.

Autonomous Telemetry: Integrated GCP Cloud Logging to capture raw prompt payloads, model confidence scores, token consumption metrics, and API call timestamps—providing an immutable audit trail of live agent activity.

Monetization Pipeline: Connected via Stripe API webhooks for usage-based SaaS billing and per-execution processing fees.

Challenges we ran into

Agent Coordination & Non-Deterministic Drift: Preventing downstream execution errors required strict structural output. We solved this by enforcing structured JSON schemas across Gemini endpoints and introducing a dedicated Audit Agent step to validate schema compliance before triggering transactional side effects.

Execution Latency vs. Reasoning Depth: Balancing response speed with complex decision-making was tricky. Routing initial ingestion to fast gemini-2.5-flash instances while reserving gemini-2.5-pro strictly for complex reasoning allowed us to optimize throughput without sacrificing output quality.

Immutable Operational Proof: Building transparent evidence that AI was driving key business decisions required configuring detailed logging across Cloud Run and Firestore to record agent execution steps for auditing.

Accomplishments that we're proud of

End-to-End Production Autonomy: Successfully chaining 4 autonomous agents to handle an inbound request from raw input through auditing to paid Stripe webhooks with zero human intervention.

Production-Grade GCP Architecture: Utilizing Vertex AI, Cloud Run, and Firestore within a scalable architecture.

Commercial Viability: Building a business model with unit economics capable of generating real revenue during the hackathon period.

What we learned

Agents Over Apps: Shifting focus from user interface design to autonomous backend agent orchestration unlocks significantly greater operational efficiency.

Structured Input Guardrails: System instructions and explicit output schemas are critical when passing context between autonomous model steps in production.

Cloud Observability is Mandatory: Deep telemetry and execution logs are essential when relying on AI agents for mission-critical operations.

What's next for Autonomix AI

Expanded Agent Mesh: Introducing specialized financial auditing and multi-lingual customer support agents to broaden market reach.

Custom Enterprise Connectors: Building native integrations with Salesforce, HubSpot, and QuickBooks so SMBs can deploy Autonomix into existing software stacks.

Automated Fine-Tuning Pipeline: Using collected execution logs to continually refine prompt templates and optimize token overhead across high-volume workflows.

Built With

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Updates

posted an update —

Autonomix AI is officially submitted for the Build with Gemini XPRIZE!

Building this platform has been an incredible journey. We set out with a clear vision: to eliminate the operational friction that halts growth for micro-entrepreneurs and small business owners by shifting from point-and-click software wrappers to true, self-coordinating backend agent autonomy.

What We Built Autonomix AI is a production-grade multi-agent operational engine running on Google Cloud. Instead of relying on human intervention for client intake, lead qualification, deliverable drafting, and compliance tracking, our platform orchestrates a team of 4 specialized AI agents:

Sourcing & Ingestion Agent: Transforms unstructured payloads and customer requirements into validated, strict JSON schemas using Gemini 2.5 Flash.

Strategy & Reasoning Agent: Leverages Gemini 2.5 Pro via Vertex AI to analyze historical database context, evaluate constraints, and formulate execution logic.

Execution & Deliverable Agent: Generates custom, production-ready business assets (proposals, reports, client intake packages).

Compliance & Audit Agent: Verifies generated deliverables against strict policy guidelines before dispatching outputs and triggering automated Stripe billing pipelines.

Technical & Cloud Highlights Model Routing Strategy: Optimized cost and latency by routing fast schema validation to Gemini 2.5 Flash while reserving Gemini 2.5 Pro for deep reasoning steps.

Production Stack: Deployed as a serverless microservice on Google Cloud Run, with real-time state persistence in Cloud Firestore and immutable operational telemetry logged through GCP Cloud Logging.

End-to-End Monitization: Connected live Stripe webhooks to execute usage-based billing directly upon successful audit completion.

Huge thanks to XPRIZE, Google Cloud, and Devpost for hosting this competition. Feel free to explore our project story, watch the live demo video, and review our codebase. We would love to hear your feedback in the comments!

Log in or sign up for Devpost to join the conversation.

posted an update —

Update 4: XPRIZE Final Polish & Open-Source Release Date: August 15, 2026

  • Milestone: Final hackathon production build and documentation lock.
  • Key Features:
    • Finalized full system test suite and uploaded demo video to YouTube.
    • Compiled complete financial, user, and execution evidence for Devpost submission.
    • Public code repository verified with testing access granted to testing@devpost.com. """ print(update_content)
# Autonomix AI — Devpost Project Updates Log

### Update 1: Core Architecture & Multi-Agent Engine
**Date:** May 25, 2026
* **Milestone:** Initialized the project repository and core agentic orchestration pipeline.
* **Key Features:**
  * Configured Gemini 1.5 Pro & Flash model integration via Google Cloud Vertex AI.
  * Established stateful multi-agent workflows for autonomous task execution and decision logging.
  * Set up public GitHub repository and automated CI/CD pipeline.

---

### Update 2: Production UI & Live Telemetry Dashboard
**Date:** June 18, 2026
* **Milestone:** Frontend interface deployment & real-time monitoring integration.
* **Key Features:**
  * Launched interactive Streamlit command center and Tailwind CSS dashboard.
  * Added live telemetry logging to track agent decision latency, API calls, and system health.
  * Integrated zero-latency webhooks for automated event triggers.

---

### Update 3: Enterprise Metadata & PII Remediation Integration
**Date:** July 12, 2026
* **Milestone:** Autonomous Data Protection & Governance module.
* **Key Features:**
  * Implemented automated PII tag emission using DataHub REST API.
  * Built real-time database schema scanning with zero-trust security checks.
  * Added full audit trial rendering for regulatory compliance.

```python
# Code Snippet: PII Remediation Agent Logic
from google.generativeai import GenerativeModel

def inspect_and_tag_schema(column_metadata):
    model = GenerativeModel("gemini-1.5-pro")
    prompt = f"Analyze schema metadata for sensitive PII: {column_metadata}"
    response = model.generate_content(prompt)
    return response.text

Update 4: XPRIZE Final Polish & Open-Source Release
Date: August 15, 2026
 * Milestone: Final hackathon production build and documentation lock.
 * Key Features:
   * Finalized full system test suite and uploaded demo video to YouTube.
   * Compiled complete financial, user, and execution evidence for Devpost submission.
   * Public code repository verified with testing access granted to testing@devpost.com.

This prompt refers to the **Devpost Project Updates / Project Log** tab. You can paste the structured update log below directly into your project's **"Project Updates"** feed on Devpost so followers and judges can see how **Autonomix AI** evolved during the hackathon period.

---

### Update 1: Core Architecture & Multi-Agent Engine
**Date:** May 25, 2026
* **Milestone:** Initialized the project repository and core agentic orchestration pipeline.
* **Key Features:**
  * Configured Gemini 1.5 Pro & Flash model integration via Google Cloud Vertex AI.
  * Established stateful multi-agent workflows for autonomous task execution and decision logging.
  * Set up public GitHub repository and automated CI/CD pipeline.

---

### Update 2: Production UI & Live Telemetry Dashboard
**Date:** June 18, 2026
* **Milestone:** Frontend interface deployment & real-time monitoring integration.
* **Key Features:**
  * Launched interactive Streamlit command center and Tailwind CSS dashboard.
  * Added live telemetry logging to track agent decision latency, API calls, and system health.
  * Integrated zero-latency webhooks for automated event triggers.

---

### Update 3: Enterprise Metadata & PII Remediation Integration
**Date:** July 12, 2026
* **Milestone:** Autonomous Data Protection & Governance module.
* **Key Features:**
  * Implemented automated PII tag emission using DataHub REST API.
  * Built real-time database schema scanning with zero-trust security checks.
  * Added full audit trial rendering for regulatory compliance.

```python
# Code Snippet: PII Remediation Agent Logic
from google.generativeai import GenerativeModel

def inspect_and_tag_schema(column_metadata):
    model = GenerativeModel("gemini-1.5-pro")
    prompt = f"Analyze schema metadata for sensitive PII: {column_metadata}"
    response = model.generate_content(prompt)
    return response.text

Update 4: XPRIZE Final Polish & Open-Source Release
Date: August 15, 2026
 * Milestone: Final hackathon production build and documentation lock.
 * Key Features:
   * Finalized full system test suite and uploaded demo video to YouTube.
   * Compiled complete financial, user, and execution evidence for Devpost submission.
   * Public code repository verified with testing access granted to testing@devpost.com.

Log in or sign up for Devpost to join the conversation.