Forge-AI Protocol v3.1
18 AI Agents. 8 Phases.
One Startup a Day.
Forge-AI is an autonomous multi-agent pipeline that runs every day at 00:00 UTC. It detects emerging tech trends, designs a micro-startup concept, writes a production-ready Spring Boot application, validates it compiles, enriches it with 17 specialist AI agents, and publishes the result here.
No human writes the code. No human writes the pitch. The pipeline handles everything — from market sizing to EU AI Act compliance — for roughly $0.005 per run.
18
AI Agents
8
Pipeline Phases
~$0.005
Cost per Run
20+
Output Files
The Pipeline
Trend Detection
parallelParallel Scout × 3
Three ScoutAgents scan the web simultaneously via Tavily, detecting the strongest emerging tech trend of the day. The best signal wins.
Outputs
- →trends.json
- →3 candidate topics
Business Case
ProductManagerAgent
The PM distills the winning trend into a full BusinessCase: startup name, problem statement, target audience, monetization angle, MVP features, and Spring Boot starters.
Outputs
- →BusinessCase.json
Parallel Incubation
parallel4 agents in StructuredTaskScope
The heaviest phase. Claude Opus generates a complete Spring Boot 4 project with all MVP features while three other agents run in parallel: market sizing, growth strategy, and cloud cost estimation. Java 25 Virtual Threads + StructuredTaskScope with a 45-minute timeout.
Outputs
- →Full Spring Boot source code
- →MarketAnalysis.json
- →MarketingMaterial.json
- →CostEstimate.json
Maven Validation + Auto-Healing
Up to 3 attempts
The generated code is compiled. On failure, ValidatorAgent (Claude) performs a surgical analysis of Maven errors and generates targeted fix prompts. ForgeEngineer corrects only the broken files — no full regen.
Outputs
- →BUILD SUCCESS or auto-healed project
Enrichment
parallel4 agents in parallel
Claude writes JUnit 5 test suites directly to disk. Ethics Officer checks EU AI Act compliance. TechWriter generates an OpenAPI 3.1 spec with curl examples. SharkTank delivers a brutal code + market review.
Outputs
- →ETHICS_GUIDE.md
- →src/test/java/*.java
- →openapi-spec.yml
- →CURL_EXAMPLES.md
- →SHARK_TANK_REVIEW.md
Simulation
parallel5 agents in parallel
Five agents simulate real-world scenarios: UX testing with 3 personas, SRE deployment planning with real cloud pricing, IP patent analysis, VC investment scoring, and cross-project synergy detection across the entire solutions/ catalog.
Outputs
- →deploy/Dockerfile
- →deploy/k8s-hpa.yml
- →SLO_REPORT.md
- →IP_STRATEGY.md
- →VC_ASSESSMENT.md
- →ECOSYSTEM_SYNERGY.md
CFO Report
CFOAgent
The CFO receives all financial signals — FinOps cost estimate, real LLM token costs from this run, market analysis — and produces unit economics: CAC, LTV, payback period, Y1-Y3 ARR projections.
Outputs
- →FINANCIAL_REPORT.md
Pitch + Chronicle + Publish
PitchAgent + ChronicleAgent
PitchAgent synthesizes all 17 agent outputs into a 30-second elevator pitch. ChronicleAgent writes a 1,500-word narrative article for The Forge Chronicles. The startup report is persisted, the README is updated, and Telegram notifications are sent.
Outputs
- →startup-report.json
- →MEETING_LOG.json
- →CHRONICLE.md
- →Telegram notification
- →README update
Model Architecture
Each agent is assigned to one of three model tiers based on the complexity of its task. This keeps costs minimal without sacrificing quality where it matters.
Claude Opus 4.6
Code generation, test writing, error healing. The architect.
Only Claude can generate 1,000+ line Spring Boot projects that compile on the first try.
Gemini 2.5 Pro
Market analysis, investment scoring, legal. The analysts.
Deep reasoning over large context windows. Used where quality of analysis matters more than speed.
Gemini 2.5 Flash
Scouts, narrators, simulators, pitch. High-volume tasks.
10× cheaper, still excellent. Used for tasks where breadth matters more than depth.
Tech Stack
Runtime
Parallel agents with structured concurrency
Agent framework with auto-JSON deserialization
All agent outputs are strongly-typed Java records
AI Models
Code generation — runs as a subprocess
Strategic analysis with exponential backoff
High-throughput tasks, wrapped in RetryingChatModel
Live web search for trend detection
Generated Startups
Scaffolded by SpringInitializrTool
AI library chosen by ProductManagerAgent
Auto-healed on failure by ValidatorAgent
Frontend & Infra
This site — reads solutions/ at build time
Auto-deploys on push to main
CRT scanlines, star field, neon glow
Java 25 Concurrency Model
The pipeline uses Java's structured concurrency primitives to run agents in parallel with strict lifetime guarantees. All forks are scoped — if one fails, the scope handles it cleanly.
// Phase 3: 4 agents running in parallel
try (var scope = StructuredTaskScope.open(
Joiner.awaitAll(), cfg => cfg.withTimeout(Duration.ofMinutes(45)))) {
scope.fork(() => agents.engineer.forge(...));
scope.fork(() => agents.marketAnalyst.analyze(...));
scope.fork(() => agents.growthAgent.strategize(...));
scope.fork(() => agents.finOps.estimate(...));
scope.join();
}
Output per Startup
Every forged startup is a self-contained directory with 20+ files. Here's what gets generated automatically in a single pipeline run.
src/main/java/**/*.java
Full Spring Boot application with all MVP features
pom.xml
Maven build — Spring Boot 4 + Spring AI + Jakarta EE
startup-report.json
All 17 agent scores aggregated into one JSON
EXPLAINER.md
Technical architecture doc written by ForgeEngineer
SHARK_TANK_REVIEW.md
Cross-model code + market review (Gemini → Claude)
VC_ASSESSMENT.md
Investment thesis, funding stage recommendation
FINANCIAL_REPORT.md
Unit economics, CAC/LTV, Y1-Y3 ARR projections
ETHICS_GUIDE.md
EU AI Act compliance, risk category, ethical guidelines
MARKETING_PLAN.md
Growth strategy, tagline, domain suggestions
openapi-spec.yml
OpenAPI 3.1 spec for all generated endpoints
CURL_EXAMPLES.md
Ready-to-run curl commands for every endpoint
IP_STRATEGY.md
Patentable claims, license analysis
SLO_REPORT.md
Service Level Objectives for production
deploy/Dockerfile
Optimised multi-stage Docker image
deploy/k8s-hpa.yml
Kubernetes HPA + deployment manifest
ECOSYSTEM_SYNERGY.md
Cross-project integration opportunities
CHRONICLE.md
1,500-word narrative article for The Forge Chronicles
MEETING_LOG.json
Full board meeting transcript — all 17 agents
AGENT_LOG.md
Incremental per-phase progress log
Why
Forge-AI is an experiment in autonomous software generation. The question is simple: how far can a pipeline of coordinated AI agents go without human intervention?
Each day, the pipeline starts from nothing — no pre-written code, no templates, no manual input. It finds a trend, designs a product, writes the software, validates it compiles, reviews it from six different professional perspectives, and prices it for acquisition — all autonomously.
The cross-model review is deliberate. Claude generates the code; Gemini reviews it as a SharkTank investor. Neither model is reviewing its own output. This adversarial setup catches what self-review misses.
The startups listed on this site are real generated artifacts — functional Spring Boot projects, not demos. The scores are real agent outputs. The financial reports are real model reasoning about real cost data.