Research.Build.Publish.Open source.
Where SocioFi pushes the boundaries of AI-native development. We research what's coming, experiment in public, and release tools the community can use.
What we're investigating.
Four sustained research programs — each with its own question, its own experiments, and its own published findings.
6 open-source tools.
Free, forever.
Every useful tool that comes out of our research goes back to the community. If we solved a hard problem, you should not have to solve it again.
Browse all repos- agent-evaltooling
- prompt-guardtooling
- rag-benchbenchmark
- spec-runnerframework
- industry-datasetsdataset
- flow-tracertooling
We publish everything, including failures.
Every experiment gets logged — hypothesis, method, result. Failed experiments are as valuable as successes. Probably more.
Autonomous code review — zero human oversight
An AI agent can serve as sole code reviewer on production code with no human approval gate.
Developer ToolingAI review + mandatory human security pass
AI handles logic and style review; human engineer handles security pass only. Faster than full human review with equivalent safety.
Developer ToolingAutomated security pattern detection in AI-generated code
A specialized security-pattern classifier can flag the categories of vulnerabilities that general review agents miss.
Developer ToolingThe 10-Agent Pipeline.
Every SocioFi project runs through ten specialized AI agents, each with a defined role, scope, and handoff protocol — refined across 45 production deployments.
Spec Agent
Converts project briefs into structured, reviewable specifications
Architecture Agent
Designs system structure, data models, and service boundaries
Scaffold Agent
Generates project skeleton — routes, configs, folder structure
Implementation Agent
Writes feature code against the architecture specification
Review Agent
Code quality, style consistency, and logic validation
Test Agent
Generates unit, integration, and regression test suites
Debug Agent
Identifies failure causes and proposes targeted fixes
Documentation Agent
Writes technical docs, API references, and inline comments
Deploy Agent
Configures infrastructure, environment variables, and pipelines
Monitor Agent
Observability setup — logging, alerting, uptime tracking
What we've been writing.
Orchestrator-Worker: The Only Multi-Agent Pattern That Scales in Production
After running 45 AI agents across three products, one coordination pattern emerged as the clear winner. Here is why orchestrator-worker works and what happens when teams try to skip it.
RAG Accuracy at Scale: What Our Benchmarks Actually Show
We ran 1,200 retrieval queries across four pipeline configurations and five corpus sizes. The results are more nuanced than the marketing materials suggest.
AI Test Generation: Real Coverage Numbers from 18 Projects
We tracked test coverage across 18 Studio projects before and after introducing AI-generated test suites. The improvement is real. The caveats matter too.
Follow the research.
We publish what we learn — including failures. Subscribe to the newsletter or browse the blog.