Technical Prospectus: The Agalmic Engine
Engineering Philosophy
AI is collapsing the cost of software production at an accelerating rate. This transformation marks the end of software as a scarce resource and the beginning of software as an automated utility. Therefore, we assume code generation is already solved; if it is not currently solved relative to our needs, it soon will be. This allows us to direct our primary energy toward the arguably harder problem of managing intelligence as opposed to producing it. We must build the automated infrastructure, deployment, and testing framework required to turn raw generation into a high-fidelity, zero-maintenance reality.
Even with high-quality code generation, several challenges remain difficult given current technologies:
- Architectural Coherence: Maintaining a unified technical vision across thousands of AI-generated patches and features.
- Agentic Orchestration: Creating the "body" (infrastructure) that can keep up with the "brain" (code generation).
- High-Fidelity Experience Modeling: Moving beyond "Happy Path" testing to ensure that updates reflect the creative desires of the community and resist balance-breaking strategies.
This document is intentionally aspirational. We recognize that while some objectives outlined here will be straightforward to achieve, others will be significantly harder, and some may ultimately prove impossible. Nevertheless, a project of this ambition requires a definitive vector of intent—a direction in which to start.
I. The Verification Loop
In a world where AIs generate the majority of code, our engine is built on an automated "Intent-to-Deployment" pipeline:
Automated Tests
Automated testing is essential. Furthermore, it is our objective—and our hope—that this automation will prove sufficient so that human-mediated quality control is never needed in the long term. We rely on two primary types of tests: automated unit tests and automated end-to-end acceptance tests. Unit tests verify core integrity and must be fast, whereas acceptance tests check the entire system and may be slow.
The Auditor Layer
A separate AI agent audits generated code for logic errors and technical alignment with the original prompt before it enters production.
Infrastructure Verification
The "Auditor Layer" extends to the environment itself. Every AI-proposed change to the hardware, network, or cost-scaling parameters is verified to ensure security, budget adherence, and mission alignment before deployment.
Cross-Platform Validation
Automated checks ensure consistent performance across web, mobile, and desktop environments.
II. AI-Driven "Creative Triage" & Playtesting
We leverage specialized AI "Player Agents" that perform deep behavioral analysis to get ahead of the meta-game:
Deep Behavioral Playtesting
We deploy AI agents designed to simulate human-like play, specifically tasked with identifying balance-breaking strategies or unfun game loops that could degrade the competitive experience.
Aesthetic & Subjective Feedback
By training AIs to recognize the qualitative preferences of specific gaming communities, we identify friction points before a human player encounters them.
Human-AI Feedback Loop
Humans remain the final arbiter; the community reviews the findings of these AI "scouts" and provides feedback on their results.
III. Dual-Track Bug Mitigation
We treat bug fixes as a distinct workstream to ensure platform stability:
| Track | Purpose |
|---|---|
| Private Disclosure | A secure channel for reporting vulnerabilities that should not be public until patched. |
| Public Triage | Non-sensitive bugs are listed in the Governance Terminal, allowing users to "indicate impact" and provide a direct priority signal for AI patching agents. |
IV. Impact Forecasting & Evolving Consensus
Proactive Impact Forecasting
When users propose a change, the Engine generates an Impact Forecast to visualize potential systemic trade-offs, providing the community with a data-driven preview of how the update might alter the ecosystem's balance and player experience.
Malleable Governance
The consensus mechanism is itself a module. The community may vote to change the feedback model as the needs of the ecosystem evolve.
V. Standardized Integrity & Portability
To ensure long-term sustainability and facilitate the "Article V" transition to the commons for discontinued games, we enforce professional-grade coding standards through automated gatekeeping:
Enforced Linting & Style
The AI-orchestrator enforces strict linting norms (e.g., Google Style/PEP docstrings). Code that does not meet these requirements is automatically rejected.
Dependency Hygiene
We prioritize the use of standard, well-maintained libraries to minimize technical debt and maximize the ease with which a community member can host or fork the project.
VI. Agentic Orchestration & Model-Driven Ops
Our long-term goal is a "Zero-Maintenance" infrastructure designed to be managed by AI agents within a closed-loop system:
Declarative "Agent-Readable" Infrastructure
We treat the entire cloud environment as Infrastructure as Code (IaC). By maintaining a declarative state, we provide the AI with a clear map of the environment, allowing it to "read" and "reason" about the system's architecture just as it does with application code.
Model-Driven Operations
Instead of manual DevOps, we leverage AI agents that ingest live observability data (metrics, logs, and traces). These agents act as autonomous site reliability engineers, optimizing database queries, managing horizontal scaling, and responding to lag spikes in real-time.
Cost-Aware Resource Management
AI agents are tasked with enforcing the Financial Waterfall. By tagging all compute resources, the Engine provides real-time reporting on the Social Dividend, ensuring that infrastructure costs are minimized to maximize the public utility's impact.
This prospectus describes the technical philosophy behind the Agalmic Engine. For the development roadmap, see the Roadmap. For the founding vision, see the Manifesto.