<!--
  Title: Miniforge.ai
  Author: Christopher Lester (christopher@miniforge.ai)
  Copyright 2025-2026 Christopher Lester. Licensed under Apache 2.0.
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# **miniforge.ai — Autonomous SDLC Platform**

## **Founder Prior-Art Record**

**Author:** Christopher Lester
**Date:** 2026-01-17

---

## 1. Purpose

This document records the existence, design, and conceptual structure of **miniforge.ai**,
an autonomous software development lifecycle (SDLC) platform, prior to and independent of
any specific employer, customer, or commercial deployment.

miniforge.ai originates from Christopher Lester’s independent work across:

* EngrammicAI
* SpicyBrain
* Prior research into multi-agent systems, cognitive feedback loops, and knowledge-graph-driven software workflows

Any implementation built inside an organization represents a **customer-specific instantiation** of this platform, not its invention.

This document exists to establish prior art for the architectural concepts, system design, and operational model described below.

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## 2. What miniforge.ai is

**miniforge.ai is a self-directing software factory that converts human intent into production-grade software through
 coordinated teams of AI agents operating over a governed, continuously improving control plane.**

Given a prompt, specification, or business objective, miniforge.ai:

* Decomposes intent into structured work
* Designs systems and interfaces
* Implements application and infrastructure code
* Generates tests and validation artifacts
* Manages releases across environments
* Observes production behavior
* Improves its own delivery process over time

miniforge.ai is not a chatbot, code generator, or CI/CD wrapper.
It is an **autonomous SDLC**.

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## 3. Core abstraction: Agent Teams

miniforge.ai is built on **multi-agent cognition**, not single-prompt generation.

Each agent represents a specialized role analogous to those found in effective engineering organizations.
Agents operate with explicit responsibilities, memory, and contracts with other agents.

Canonical agent roles include:

| Agent       | Responsibility                                       |
| ----------- | ---------------------------------------------------- |
| Planner     | Translates goals into executable work plans          |
| Architect   | Designs system structure, boundaries, and interfaces |
| Implementer | Produces application and infrastructure code         |
| Tester      | Generates tests and validation strategies            |
| Reviewer    | Enforces quality, correctness, and standards         |
| SRE         | Manages deployment, reliability, and rollback        |
| Security    | Threat modeling and policy enforcement               |
| Release     | Controls promotion across environments               |
| Historian   | Records decisions, outcomes, and learning            |
| Operator    | Manages the self improvment loop and all subflows    |

Agents communicate through **structured artifacts and shared state**, not ad-hoc conversation.

This model derives from SpicyBrain’s cognitive-graph approach: distributed intelligence composed of cooperating, specialized nodes.

---

## 4. The Control Plane

miniforge.ai is governed by a **control plane** that orchestrates agents, artifacts, and policies.

The control plane is responsible for:

* Work scheduling and orchestration
* Agent coordination and handoff
* Policy enforcement and governance
* Artifact provenance and traceability
* Cost, latency, and risk budgets
* Escalation and failure handling

Conceptually, the control plane is analogous to:

> Kubernetes for software production

It manages *how* software is built, not just *what* is built.

---

## 5. Artifact Graph

miniforge.ai operates on **artifacts**, not files.

Artifacts include:

* Specifications and requirements
* Architecture Decision Records (ADRs)
* Source code
* Tests and validation outputs
* Container images
* Infrastructure definitions
* Deployment manifests
* Telemetry and incident records
* Documentation

Every artifact is:

* Versioned
* Attributed to agents and decisions
* Traceable back to originating intent
* Linked within a dependency graph

This structure originates from EngrammicAI’s knowledge-graph and journaling architecture.

---

## 6. Loop Semantics (Inner, Outer, Meta)

miniforge.ai is defined by **nested control loops**.
It is not a linear pipeline.

### 6.1 Inner Loop — Artifact Production

**Objective:** produce a correct, reviewable artifact increment.

**Scope:** per change / per task

**Cycle:**

* Generate → Validate → Repair

Validation may include:

* Compilation
* Linting
* Tests
* Security scans
* Policy checks

The loop terminates when:

* Local quality gates are satisfied, or
* Escalation is triggered due to ambiguity or repeated failure

---

### 6.2 Outer Loop — SDLC Delivery

**Objective:** deliver a coherent slice from intent to production.

**Scope:** per feature / per specification

**Cycle:**

* Plan → Design → Implement → Verify → Review → Release → Observe

Outputs are first-class artifacts, not transient steps.
Traceability from intent to deployment is preserved.

The loop terminates on:

* Successful production release, or
* Explicit abandonment or rollback

---

### 6.3 Meta Loop — SDLC Self-Improvement

**Objective:** improve the *process* that runs the inner and outer loops.

The meta loop operates across runs and observes signals such as:

* Recurrent review feedback
* Repeated failure modes
* Test gaps discovered post-release
* Incident and rollback patterns
* Human overrides and corrections
* Cost, latency, and throughput metrics

Based on these signals, the meta loop may propose or enact changes to:

* Agent role definitions and prompts
* Planning and decomposition heuristics
* Review and validation policies
* Safety rails and escalation rules
* Release strategies and gating thresholds

All meta-loop changes are:

* Versioned
* Auditable
* Reversible

Governance may require:

* Human approval
* Shadow-mode evaluation
* Canary rollout

Without the meta loop, miniforge.ai is automation.
With it, miniforge.ai is an **evolving software factory**.

---

## 7. Safety and Governance

miniforge.ai includes explicit congitive safety governance mechanisms:

* Approval gates
* Blast-radius limits
* Rollback authority
* Audit trails
* Configurable human-in-the-loop controls

The system is designed to be:

> More inspectable and governable than human-only processes

This makes it suitable for regulated and security-sensitive environments.

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## 8. Platform vs. Deployment

miniforge.ai is a **platform**.

A customer deployment includes:

* A configured agent set
* Organization-specific repositories
* Policies, thresholds, and workflows

The deployment is an **instance** of miniforge.ai, not the platform itself.

---

## 9. Ownership and Prior Art

The architectural concepts documented here — including agent teams, control-plane orchestration, artifact graphs,
nested control loops, and SDLC self-improvement — originate from Christopher Lester’s independent work prior to
and outside of any employer engagement.

Employer or customer implementations represent operational instantiations of these ideas, not their origin.

---

## 10. Summary

miniforge.ai is:

> An industrial software factory that fits on your desk.

It industrializes software delivery through autonomous agent teams while preserving developer control, auditability, and governance.
